Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis,
A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M.,
Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R.,
Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I.,
Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden,
P., Wattenberg, M., Wicke, M., Yu, Y., and Zheng, X. (2015). TensorFlow: Large-scale
machine learning on heterogeneous systems. Software available from 25,
210, 441
Ackley, D. H., Hinton, G. E., and Sejnowski, T. J. (1985). A learning algorithm for
Boltzmann machines. Cognitive Science, 9, 147–169. 567, 651
Alain, G. and Bengio, Y. (2013). What regularized auto-encoders learn from the data
generating distribution. In ICLR’2013, arXiv:1211.4246 . 504, 509, 512, 518
Alain, G., Bengio, Y., Yao, L., Éric Thibodeau-Laufer, Yosinski, J., and Vincent, P. (2015).
GSNs: Generative stochastic networks. arXiv:1503.05571. 507, 709
Allen, R. B. (1987). Several studies on natural language and back-propagation. In IEEE
First International Conference on Neural Networks, volume 2, pages 335–341, San
Diego. 468
Anderson, E. (1935). The Irises of the Gaspé Peninsula. Bulletin of the American Iris
Society, 59, 2–5. 19
Ba, J., Mnih, V., and Kavukcuoglu, K. (2014). Multiple object recognition with visual
attention. arXiv:1412.7755 . 688
Bachman, P. and Precup, D. (2015). Variational generative stochastic networks with
collaborative shaping. In Proceedings of the 32nd International Conference on Machine
Learning, ICML 2015, Lille, France, 6-11 July 2015 , pages 1964–1972. 713
Bacon, P.-L., Bengio, E., Pineau, J., and Precup, D. (2015). Conditional computation in
neural networks using a decision-theoretic approach. In 2nd Multidisciplinary Conference
on Reinforcement Learning and Decision Making (RLDM 2015). 445
Bagnell, J. A. and Bradley, D. M. (2009). Differentiable sparse coding. In D. Koller,
D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural Information
Processing Systems 21 (NIPS’08), pages 113–120. 494
Bahdanau, D., Cho, K., and Bengio, Y. (2015). Neural machine translation by jointly
learning to align and translate. In ICLR’2015, arXiv:1409.0473 . 25, 99, 392, 412, 415,
459, 470, 471
Bahl, L. R., Brown, P., de Souza, P. V., and Mercer, R. L. (1987). Speech recognition
with continuous-parameter hidden Markov models. Computer, Speech and Language,
219–234. 453
Baldi, P. and Hornik, K. (1989). Neural networks and principal component analysis:
Learning from examples without local minima. Neural Networks, 2, 53–58. 283
Baldi, P., Brunak, S., Frasconi, P., Soda, G., and Pollastri, G. (1999). Exploiting the
past and the future in protein secondary structure prediction. Bioinformatics,
937–946. 388
Baldi, P., Sadowski, P., and Whiteson, D. (2014). Searching for exotic particles in
high-energy physics with deep learning. Nature communications, 5. 26
Ballard, D. H., Hinton, G. E., and Sejnowski, T. J. (1983). Parallel vision computation.
Nature. 447
Barlow, H. B. (1989). Unsupervised learning. Neural Computation, 1, 295–311. 144
Barron, A. E. (1993). Universal approximation bounds for superpositions of a sigmoidal
function. IEEE Trans. on Information Theory, 39, 930–945. 195
Bartholomew, D. J. (1987). Latent variable models and factor analysis. Oxford University
Press. 486
Basilevsky, A. (1994). Statistical Factor Analysis and Related Methods: Theory and
Applications. Wiley. 486
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I. J., Bergeron, A.,
Bouchard, N., and Bengio, Y. (2012). Theano: new features and speed improvements.
Deep Learning and Unsupervised Feature Learning NIPS 2012 Workshop. 25, 80, 210,
218, 441
Basu, S. and Christensen, J. (2013). Teaching classification boundaries to humans. In
AAAI’2013 . 325
Baxter, J. (1995). Learning internal representations. In Proceedings of the 8th International
Conference on Computational Learning Theory (COLT’95), pages 311–320, Santa Cruz,
California. ACM Press. 241
Bayer, J. and Osendorfer, C. (2014). Learning stochastic recurrent networks. ArXiv
e-prints. 262
Becker, S. and Hinton, G. (1992). A self-organizing neural network that discovers surfaces
in random-dot stereograms. Nature, 355, 161–163. 539
Behnke, S. (2001). Learning iterative image reconstruction in the neural abstraction
pyramid. Int. J. Computational Intelligence and Applications, 1(4), 427–438. 511
Beiu, V., Quintana, J. M., and Avedillo, M. J. (2003). VLSI implementations of threshold
logic-a comprehensive survey. Neural Networks, IEEE Transactions on,
(5), 1217–
1243. 446
Belkin, M. and Niyogi, P. (2002). Laplacian eigenmaps and spectral techniques for
embedding and clustering. In T. Dietterich, S. Becker, and Z. Ghahramani, editors,
Advances in Neural Information Processing Systems 14 (NIPS’01), Cambridge, MA.
MIT Press. 240
Belkin, M. and Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and
data representation. Neural Computation, 15(6), 1373–1396. 160, 516
Bengio, E., Bacon, P.-L., Pineau, J., and Precup, D. (2015a). Conditional computation in
neural networks for faster models. arXiv:1511.06297. 445
Bengio, S. and Bengio, Y. (2000a). Taking on the curse of dimensionality in joint
distributions using neural networks. IEEE Transactions on Neural Networks, special
issue on Data Mining and Knowledge Discovery, 11(3), 550–557. 703
Bengio, S., Vinyals, O., Jaitly, N., and Shazeer, N. (2015b). Scheduled sampling for
sequence prediction with recurrent neural networks. Technical report, arXiv:1506.03099.
Bengio, Y. (1991). Artificial Neural Networks and their Application to Sequence Recognition.
Ph.D. thesis, McGill University, (Computer Science), Montreal, Canada. 402
Bengio, Y. (2000). Gradient-based optimization of hyperparameters. Neural Computation,
12(8), 1889–1900. 430
Bengio, Y. (2002). New distributed probabilistic language models. Technical Report 1215,
Dept. IRO, Université de Montréal. 462
Bengio, Y. (2009). Learning deep architectures for AI . Now Publishers. 197, 621
Bengio, Y. (2013). Deep learning of representations: looking forward. In Statistical
Language and Speech Processing, volume 7978 of Lecture Notes in Computer Science,
pages 1–37. Springer, also in arXiv at 443
Bengio, Y. (2015). Early inference in energy-based models approximates back-propagation.
Technical Report arXiv:1510.02777, Universite de Montreal. 653
Bengio, Y. and Bengio, S. (2000b). Modeling high-dimensional discrete data with multi-
layer neural networks. In NIPS 12 , pages 400–406. MIT Press. 702, 703, 705, 707
Bengio, Y. and Delalleau, O. (2009). Justifying and generalizing contrastive divergence.
Neural Computation, 21(6), 1601–1621. 509, 609
Bengio, Y. and Grandvalet, Y. (2004). No unbiased estimator of the variance of k-fold
cross-validation. In S. Thrun, L. Saul, and B. Schölkopf, editors, Advances in Neural
Information Processing Systems 16 (NIPS’03), Cambridge, MA. MIT Press, Cambridge.
Bengio, Y. and LeCun, Y. (2007). Scaling learning algorithms towards AI. In Large Scale
Kernel Machines. 18
Bengio, Y. and Monperrus, M. (2005). Non-local manifold tangent learning. In L. Saul,
Y. Weiss, and L. Bottou, editors, Advances in Neural Information Processing Systems
17 (NIPS’04), pages 129–136. MIT Press. 157, 518
Bengio, Y. and Sénécal, J.-S. (2003). Quick training of probabilistic neural nets by
importance sampling. In Proceedings of AISTATS 2003 . 465
Bengio, Y. and Sénécal, J.-S. (2008). Adaptive importance sampling to accelerate training
of a neural probabilistic language model. IEEE Trans. Neural Networks,
(4), 713–722.
Bengio, Y., De Mori, R., Flammia, G., and Kompe, R. (1991). Phonetically motivated
acoustic parameters for continuous speech recognition using artificial neural networks.
In Proceedings of EuroSpeech’91 . 23, 454
Bengio, Y., De Mori, R., Flammia, G., and Kompe, R. (1992). Neural network-Gaussian
mixture hybrid for speech recognition or density estimation. In NIPS 4 , pages 175–182.
Morgan Kaufmann. 454
Bengio, Y., Frasconi, P., and Simard, P. (1993). The problem of learning long-term
dependencies in recurrent networks. In IEEE International Conference on Neural
Networks, pages 1183–1195, San Francisco. IEEE Press. (invited paper). 398
Bengio, Y., Simard, P., and Frasconi, P. (1994). Learning long-term dependencies with
gradient descent is difficult. IEEE Tr. Neural Nets. 17, 396, 398, 399, 407
Bengio, Y., Latendresse, S., and Dugas, C. (1999). Gradient-based learning of hyper-
parameters. Learning Conference, Snowbird. 430
Bengio, Y., Ducharme, R., and Vincent, P. (2001). A neural probabilistic language model.
In T. K. Leen, T. G. Dietterich, and V. Tresp, editors, NIPS’2000 , pages 932–938. MIT
Press. 17, 442, 458, 461, 467, 472, 477
Bengio, Y., Ducharme, R., Vincent, P., and Jauvin, C. (2003). A neural probabilistic
language model. JMLR, 3, 1137–1155. 461, 467
Bengio, Y., Le Roux, N., Vincent, P., Delalleau, O., and Marcotte, P. (2006a). Convex
neural networks. In NIPS’2005 , pages 123–130. 255
Bengio, Y., Delalleau, O., and Le Roux, N. (2006b). The curse of highly variable functions
for local kernel machines. In NIPS’2005 . 155
Bengio, Y., Larochelle, H., and Vincent, P. (2006c). Non-local manifold Parzen windows.
In NIPS’2005 . MIT Press. 157, 517
Bengio, Y., Lamblin, P., Popovici, D., and Larochelle, H. (2007). Greedy layer-wise
training of deep networks. In NIPS’2006 . 13, 18, 197, 319, 320, 526, 528
Bengio, Y., Louradour, J., Collobert, R., and Weston, J. (2009). Curriculum learning. In
ICML’09 . 324
Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S. (2013a). Better mixing via deep
representations. In ICML’2013 . 601
Bengio, Y., Léonard, N., and Courville, A. (2013b). Estimating or propagating gradients
through stochastic neurons for conditional computation. arXiv:1308.3432. 443, 445,
685, 688
Bengio, Y., Yao, L., Alain, G., and Vincent, P. (2013c). Generalized denoising auto-
encoders as generative models. In NIPS’2013 . 504, 708, 709
Bengio, Y., Courville, A., and Vincent, P. (2013d). Representation learning: A review and
new perspectives. IEEE Trans. Pattern Analysis and Machine Intelligence (PAMI),
35(8), 1798–1828. 552
Bengio, Y., Thibodeau-Laufer, E., Alain, G., and Yosinski, J. (2014). Deep generative
stochastic networks trainable by backprop. In ICML’2014 . 708, 709, 710, 711
Bennett, C. (1976). Efficient estimation of free energy differences from Monte Carlo data.
Journal of Computational Physics, 22(2), 245–268. 627
Bennett, J. and Lanning, S. (2007). The Netflix prize. 475
Berger, A. L., Della Pietra, V. J., and Della Pietra, S. A. (1996). A maximum entropy
approach to natural language processing. Computational Linguistics, 22, 39–71. 468
Berglund, M. and Raiko, T. (2013). Stochastic gradient estimate variance in contrastive
divergence and persistent contrastive divergence. CoRR, abs/1312.6002. 612
Bergstra, J. (2011). Incorporating Complex Cells into Neural Networks for Pattern
Classification. Ph.D. thesis, Université de Montréal. 252
Bergstra, J. and Bengio, Y. (2009). Slow, decorrelated features for pretraining complex
cell-like networks. In NIPS’2009 . 490
Bergstra, J. and Bengio, Y. (2012). Random search for hyper-parameter optimization. J.
Machine Learning Res., 13, 281–305. 428, 429
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian,
J., Warde-Farley, D., and Bengio, Y. (2010). Theano: a CPU and GPU math expression
compiler. In Proc. SciPy. 25, 80, 210, 218, 441
Bergstra, J., Bardenet, R., Bengio, Y., and Kégl, B. (2011). Algorithms for hyper-parameter
optimization. In NIPS’2011 . 430
Berkes, P. and Wiskott, L. (2005). Slow feature analysis yields a rich repertoire of complex
cell properties. Journal of Vision, 5(6), 579–602. 491
Bertsekas, D. P. and Tsitsiklis, J. (1996). Neuro-Dynamic Programming. Athena Scientific.
Besag, J. (1975). Statistical analysis of non-lattice data. The Statistician,
(3), 179–195.
Bishop, C. M. (1994). Mixture density networks. 185
Bishop, C. M. (1995a). Regularization and complexity control in feed-forward networks.
In Proceedings International Conference on Artificial Neural Networks ICANN’95 ,
volume 1, page 141–148. 238, 247
Bishop, C. M. (1995b). Training with noise is equivalent to Tikhonov regularization.
Neural Computation, 7(1), 108–116. 238
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. 96, 142
Blum, A. L. and Rivest, R. L. (1992). Training a 3-node neural network is NP-complete.
Blumer, A., Ehrenfeucht, A., Haussler, D., and Warmuth, M. K. (1989). Learnability and
the Vapnik–Chervonenkis dimension. Journal of the ACM , 36(4), 929––865. 112
Bonnet, G. (1964). Transformations des signaux aléatoires à travers les systèmes non
linéaires sans mémoire. Annales des Télécommunications, 19(9–10), 203–220. 685
Bordes, A., Weston, J., Collobert, R., and Bengio, Y. (2011). Learning structured
embeddings of knowledge bases. In AAAI 2011 . 479
Bordes, A., Glorot, X., Weston, J., and Bengio, Y. (2012). Joint learning of words and
meaning representations for open-text semantic parsing. AISTATS’2012 . 396, 479, 480
Bordes, A., Glorot, X., Weston, J., and Bengio, Y. (2013a). A semantic matching energy
function for learning with multi-relational data. Machine Learning: Special Issue on
Learning Semantics. 479
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O. (2013b).
Translating embeddings for modeling multi-relational data. In C. Burges, L. Bottou,
M. Welling, Z. Ghahramani, and K. Weinberger, editors, Advances in Neural Information
Processing Systems 26 , pages 2787–2795. Curran Associates, Inc. 479
Bornschein, J. and Bengio, Y. (2015). Reweighted wake-sleep. In ICLR’2015,
arXiv:1406.2751 . 690
Bornschein, J., Shabanian, S., Fischer, A., and Bengio, Y. (2015). Training bidirectional
Helmholtz machines. Technical report, arXiv:1506.03877. 690
Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). A training algorithm for opti-
mal margin classifiers. In COLT ’92: Proceedings of the fifth annual workshop on
Computational learning theory, pages 144–152, New York, NY, USA. ACM. 17, 139
Bottou, L. (1998). Online algorithms and stochastic approximations. In D. Saad, editor,
Online Learning in Neural Networks. Cambridge University Press, Cambridge, UK. 292
Bottou, L. (2011). From machine learning to machine reasoning. Technical report,
arXiv.1102.1808. 394, 396
Bottou, L. (2015). Multilayer neural networks. Deep Learning Summer School. 434
Bottou, L. and Bousquet, O. (2008). The tradeoffs of large scale learning. In NIPS’2008 .
279, 292
Boulanger-Lewandowski, N., Bengio, Y., and Vincent, P. (2012). Modeling temporal
dependencies in high-dimensional sequences: Application to polyphonic music generation
and transcription. In ICML’12 . 682
Boureau, Y., Ponce, J., and LeCun, Y. (2010). A theoretical analysis of feature pooling in
vision algorithms. In Proc. International Conference on Machine learning (ICML’10).
Boureau, Y., Le Roux, N., Bach, F., Ponce, J., and LeCun, Y. (2011). Ask the locals:
multi-way local pooling for image recognition. In Proc. International Conference on
Computer Vision (ICCV’11). IEEE. 339
Bourlard, H. and Kamp, Y. (1988). Auto-association by multilayer perceptrons and
singular value decomposition. Biological Cybernetics, 59, 291–294. 499
Bourlard, H. and Wellekens, C. (1989). Speech pattern discrimination and multi-layered
perceptrons. Computer Speech and Language, 3, 1–19. 454
Boyd, S. and Vandenberghe, L. (2004). Convex Optimization. Cambridge University
Press, New York, NY, USA. 91
Brady, M. L., Raghavan, R., and Slawny, J. (1989). Back-propagation fails to separate
where perceptrons succeed. IEEE Transactions on Circuits and Systems,
, 665–674.
Brakel, P., Stroobandt, D., and Schrauwen, B. (2013). Training energy-based models for
time-series imputation. Journal of Machine Learning Research,
, 2771–2797. 671,
Brand, M. (2003). Charting a manifold. In NIPS’2002 , pages 961–968. MIT Press. 160,
Breiman, L. (1994). Bagging predictors. Machine Learning, 24(2), 123–140. 253
Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). Classification and
Regression Trees. Wadsworth International Group, Belmont, CA. 142
Bridle, J. S. (1990). Alphanets: a recurrent ‘neural’ network architecture with a hidden
Markov model interpretation. Speech Communication, 9(1), 83–92. 182
Briggman, K., Denk, W., Seung, S., Helmstaedter, M. N., and Turaga, S. C. (2009).
Maximin affinity learning of image segmentation. In NIPS’2009 , pages 1865–1873. 353
Brown, P. F., Cocke, J., Pietra, S. A. D., Pietra, V. J. D., Jelinek, F., Lafferty, J. D.,
Mercer, R. L., and Roossin, P. S. (1990). A statistical approach to machine translation.
Computational linguistics, 16(2), 79–85. 19
Brown, P. F., Pietra, V. J. D., DeSouza, P. V., Lai, J. C., and Mercer, R. L. (1992). Class-
based n-gram models of natural language. Computational Linguistics,
, 467–479.
Bryson, A. and Ho, Y. (1969). Applied optimal control: optimization, estimation, and
control. Blaisdell Pub. Co. 221
Bryson, Jr., A. E. and Denham, W. F. (1961). A steepest-ascent method for solving
optimum programming problems. Technical Report BR-1303, Raytheon Company,
Missle and Space Division. 221
Buciluˇa, C., Caruana, R., and Niculescu-Mizil, A. (2006). Model compression. In
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery
and data mining, pages 535–541. ACM. 443
Burda, Y., Grosse, R., and Salakhutdinov, R. (2015). Importance weighted autoencoders.
arXiv preprint arXiv:1509.00519 . 695
Cai, M., Shi, Y., and Liu, J. (2013). Deep maxout neural networks for speech recognition.
In Automatic Speech Recognition and Understanding (ASRU), 2013 IEEE Workshop
on, pages 291–296. IEEE. 190
Carreira-Perpiñan, M. A. and Hinton, G. E. (2005). On contrastive divergence learning.
In R. G. Cowell and Z. Ghahramani, editors, Proceedings of the Tenth International
Workshop on Artificial Intelligence and Statistics (AISTATS’05), pages 33–40. Society
for Artificial Intelligence and Statistics. 609
Caruana, R. (1993). Multitask connectionist learning. In Proc. 1993 Connectionist Models
Summer School, pages 372–379. 241
Cauchy, A. (1847). Méthode générale pour la résolution de systèmes d’équations simul-
tanées. In Compte rendu des ances de l’académie des sciences, pages 536–538. 81,
Cayton, L. (2005). Algorithms for manifold learning. Technical Report CS2008-0923,
UCSD. 160
Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: A survey. ACM
computing surveys (CSUR), 41(3), 15. 100
Chapelle, O., Weston, J., and Schölkopf, B. (2003). Cluster kernels for semi-supervised
learning. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural
Information Processing Systems 15 (NIPS’02), pages 585–592, Cambridge, MA. MIT
Press. 240
Chapelle, O., Schölkopf, B., and Zien, A., editors (2006). Semi-Supervised Learning. MIT
Press, Cambridge, MA. 240, 539
Chellapilla, K., Puri, S., and Simard, P. (2006). High Performance Convolutional Neural
Networks for Document Processing. In Guy Lorette, editor, Tenth International
Workshop on Frontiers in Handwriting Recognition, La Baule (France). Université de
Rennes 1, Suvisoft. 22, 23, 440
Chen, B., Ting, J.-A., Marlin, B. M., and de Freitas, N. (2010). Deep learning of invariant
spatio-temporal features from video. NIPS*2010 Deep Learning and Unsupervised
Feature Learning Workshop. 354
Chen, S. F. and Goodman, J. T. (1999). An empirical study of smoothing techniques for
language modeling. Computer, Speech and Language, 13(4), 359–393. 457, 468
Chen, T., Du, Z., Sun, N., Wang, J., Wu, C., Chen, Y., and Temam, O. (2014a). DianNao:
A small-footprint high-throughput accelerator for ubiquitous machine-learning. In Pro-
ceedings of the 19th international conference on Architectural support for programming
languages and operating systems, pages 269–284. ACM. 446
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C.,
and Zhang, Z. (2015). MXNet: A flexible and efficient machine learning library for
heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 . 25
Chen, Y., Luo, T., Liu, S., Zhang, S., He, L., Wang, J., Li, L., Chen, T., Xu, Z., Sun, N.,
et al. (2014b). DaDianNao: A machine-learning supercomputer. In Microarchitecture
(MICRO), 2014 47th Annual IEEE/ACM International Symposium on, pages 609–622.
IEEE. 446
Chilimbi, T., Suzue, Y., Apacible, J., and Kalyanaraman, K. (2014). Project Adam:
Building an efficient and scalable deep learning training system. In 11th USENIX
Symposium on Operating Systems Design and Implementation (OSDI’14). 442
Cho, K., Raiko, T., and Ilin, A. (2010). Parallel tempering is efficient for learning restricted
Boltzmann machines. In IJCNN’2010 . 601, 612
Cho, K., Raiko, T., and Ilin, A. (2011). Enhanced gradient and adaptive learning rate for
training restricted Boltzmann machines. In ICML’2011 , pages 105–112. 670
Cho, K., van Merriënboer, B., Gulcehre, C., Bougares, F., Schwenk, H., and Bengio, Y.
(2014a). Learning phrase representations using RNN encoder-decoder for statistical
machine translation. In Proceedings of the Empiricial Methods in Natural Language
Processing (EMNLP 2014). 390, 469, 470
Cho, K., Van Merriënboer, B., Bahdanau, D., and Bengio, Y. (2014b). On the prop-
erties of neural machine translation: Encoder-decoder approaches. ArXiv e-prints,
abs/1409.1259. 407
Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B., and LeCun, Y. (2014). The
loss surface of multilayer networks. 282, 283
Chorowski, J., Bahdanau, D., Cho, K., and Bengio, Y. (2014). End-to-end continuous
speech recognition using attention-based recurrent NN: First results. arXiv:1412.1602.
Chrisman, L. (1991). Learning recursive distributed representations for holistic computa-
tion. Connection Science, 3(4), 345–366. 468
Christianson, B. (1992). Automatic Hessians by reverse accumulation. IMA Journal of
Numerical Analysis, 12(2), 135–150. 220
Chrupala, G., Kadar, A., and Alishahi, A. (2015). Learning language through pictures.
arXiv 1506.03694. 407
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y. (2014). Empirical evaluation of gated
recurrent neural networks on sequence modeling. NIPS’2014 Deep Learning workshop,
arXiv 1412.3555. 407, 455
Chung, J., Gülçehre, Ç., Cho, K., and Bengio, Y. (2015a). Gated feedback recurrent
neural networks. In ICML’15 . 407
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A., and Bengio, Y. (2015b). A
recurrent latent variable model for sequential data. In NIPS’2015 . 694
Ciresan, D., Meier, U., Masci, J., and Schmidhuber, J. (2012). Multi-column deep neural
network for traffic sign classification. Neural Networks, 32, 333–338. 24, 197
Ciresan, D. C., Meier, U., Gambardella, L. M., and Schmidhuber, J. (2010). Deep big
simple neural nets for handwritten digit recognition. Neural Computation,
, 1–14.
22, 23, 441
Coates, A. and Ng, A. Y. (2011). The importance of encoding versus training with sparse
coding and vector quantization. In ICML’2011 . 23, 252, 494
Coates, A., Lee, H., and Ng, A. Y. (2011). An analysis of single-layer networks in
unsupervised feature learning. In Proceedings of the Thirteenth International Conference
on Artificial Intelligence and Statistics (AISTATS 2011). 357, 450
Coates, A., Huval, B., Wang, T., Wu, D., Catanzaro, B., and Andrew, N. (2013).
Deep learning with COTS HPC systems. In S. Dasgupta and D. McAllester, editors,
Proceedings of the 30th International Conference on Machine Learning (ICML-13),
volume 28 (3), pages 1337–1345. JMLR Workshop and Conference Proceedings. 22, 23,
358, 442
Cohen, N., Sharir, O., and Shashua, A. (2015). On the expressive power of deep learning:
A tensor analysis. arXiv:1509.05009. 552
Collobert, R. (2004). Large Scale Machine Learning. Ph.D. thesis, Université de Paris VI,
LIP6. 193
Collobert, R. (2011). Deep learning for efficient discriminative parsing. In AISTATS’2011 .
99, 473
Collobert, R. and Weston, J. (2008a). A unified architecture for natural language processing:
Deep neural networks with multitask learning. In ICML’2008 . 466, 473
Collobert, R. and Weston, J. (2008b). A unified architecture for natural language
processing: Deep neural networks with multitask learning. In ICML’2008 . 533
Collobert, R., Bengio, S., and Bengio, Y. (2001). A parallel mixture of SVMs for very
large scale problems. Technical Report IDIAP-RR-01-12, IDIAP. 445
Collobert, R., Bengio, S., and Bengio, Y. (2002). Parallel mixture of SVMs for very large
scale problems. Neural Computation, 14(5), 1105–1114. 445
Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., and Kuksa, P. (2011a).
Natural language processing (almost) from scratch. The Journal of Machine Learning
Research, 12, 2493–2537. 324, 473, 533, 534
Collobert, R., Kavukcuoglu, K., and Farabet, C. (2011b). Torch7: A Matlab-like environ-
ment for machine learning. In BigLearn, NIPS Workshop. 25, 209, 441
Comon, P. (1994). Independent component analysis - a new concept? Signal Processing,
36, 287–314. 487
Cortes, C. and Vapnik, V. (1995). Support vector networks. Machine Learning,
273–297. 17, 139
Couprie, C., Farabet, C., Najman, L., and LeCun, Y. (2013). Indoor semantic segmentation
using depth information. In International Conference on Learning Representations
(ICLR2013). 24, 197
Courbariaux, M., Bengio, Y., and David, J.-P. (2015). Low precision arithmetic for deep
learning. In Arxiv:1412.7024, ICLR’2015 Workshop. 447
Courville, A., Bergstra, J., and Bengio, Y. (2011). Unsupervised models of images by
spike-and-slab RBMs. In ICML’11 . 558, 677
Courville, A., Desjardins, G., Bergstra, J., and Bengio, Y. (2014). The spike-and-slab
RBM and extensions to discrete and sparse data distributions. Pattern Analysis and
Machine Intelligence, IEEE Transactions on, 36(9), 1874–1887. 679
Cover, T. M. and Thomas, J. A. (2006). Elements of Information Theory, 2nd Edition.
Wiley-Interscience. 71
Cox, D. and Pinto, N. (2011). Beyond simple features: A large-scale feature search
approach to unconstrained face recognition. In Automatic Face & Gesture Recognition
and Workshops (FG 2011), 2011 IEEE International Conference on, pages 8–15. IEEE.
Cramér, H. (1946). Mathematical methods of statistics. Princeton University Press. 133,
Crick, F. H. C. and Mitchison, G. (1983). The function of dream sleep. Nature,
111–114. 607
Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics
of Control, Signals, and Systems, 2, 303–314. 194
Dahl, G. E., Ranzato, M., Mohamed, A., and Hinton, G. E. (2010). Phone recognition
with the mean-covariance restricted Boltzmann machine. In NIPS’2010 . 24
Dahl, G. E., Yu, D., Deng, L., and Acero, A. (2012). Context-dependent pre-trained deep
neural networks for large vocabulary speech recognition. IEEE Transactions on Audio,
Speech, and Language Processing, 20(1), 33–42. 454
Dahl, G. E., Sainath, T. N., and Hinton, G. E. (2013). Improving deep neural networks
for LVCSR using rectified linear units and dropout. In ICASSP’2013 . 454
Dahl, G. E., Jaitly, N., and Salakhutdinov, R. (2014). Multi-task neural networks for
QSAR predictions. arXiv:1406.1231. 26
Dauphin, Y. and Bengio, Y. (2013). Stochastic ratio matching of RBMs for sparse
high-dimensional inputs. In NIPS26 . NIPS Foundation. 617
Dauphin, Y., Glorot, X., and Bengio, Y. (2011). Large-scale learning of embeddings with
reconstruction sampling. In ICML’2011 . 466
Dauphin, Y., Pascanu, R., Gulcehre, C., Cho, K., Ganguli, S., and Bengio, Y. (2014).
Identifying and attacking the saddle point problem in high-dimensional non-convex
optimization. In NIPS’2014 . 282, 283, 284
Davis, A., Rubinstein, M., Wadhwa, N., Mysore, G., Durand, F., and Freeman, W. T.
(2014). The visual microphone: Passive recovery of sound from video. ACM Transactions
on Graphics (Proc. SIGGRAPH), 33(4), 79:1–79:10. 447
Dayan, P. (1990). Reinforcement comparison. In Connectionist Models: Proceedings of
the 1990 Connectionist Summer School , San Mateo, CA. 688
Dayan, P. and Hinton, G. E. (1996). Varieties of Helmholtz machine. Neural Networks,
9(8), 1385–1403. 689
Dayan, P., Hinton, G. E., Neal, R. M., and Zemel, R. S. (1995). The Helmholtz machine.
Neural computation, 7(5), 889–904. 689
Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Le, Q., Mao, M., Ranzato, M.,
Senior, A., Tucker, P., Yang, K., and Ng, A. Y. (2012). Large scale distributed deep
networks. In NIPS’2012 . 25, 442
Dean, T. and Kanazawa, K. (1989). A model for reasoning about persistence and causation.
Computational Intelligence, 5(3), 142–150. 659
Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., and Harshman, R. (1990).
Indexing by latent semantic analysis. Journal of the American Society for Information
Science, 41(6), 391–407. 472, 477
Delalleau, O. and Bengio, Y. (2011). Shallow vs. deep sum-product networks. In NIPS.
18, 551
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L. (2009). ImageNet: A
Large-Scale Hierarchical Image Database. In CVPR09 . 19
Deng, J., Berg, A. C., Li, K., and Fei-Fei, L. (2010a). What does classifying more than
10,000 image categories tell us? In Proceedings of the 11th European Conference on
Computer Vision: Part V , ECCV’10, pages 71–84, Berlin, Heidelberg. Springer-Verlag.
Deng, L. and Yu, D. (2014). Deep learning methods and applications. Foundations and
Trends in Signal Processing. 455
Deng, L., Seltzer, M., Yu, D., Acero, A., Mohamed, A., and Hinton, G. (2010b). Binary
coding of speech spectrograms using a deep auto-encoder. In Interspeech 2010 , Makuhari,
Chiba, Japan. 24
Denil, M., Bazzani, L., Larochelle, H., and de Freitas, N. (2012). Learning where to attend
with deep architectures for image tracking. Neural Computation,
(8), 2151–2184. 361
Denton, E., Chintala, S., Szlam, A., and Fergus, R. (2015). Deep generative image models
using a Laplacian pyramid of adversarial networks. NIPS . 698, 699, 714
Desjardins, G. and Bengio, Y. (2008). Empirical evaluation of convolutional RBMs for
vision. Technical Report 1327, Département d’Informatique et de Recherche Opéra-
tionnelle, Université de Montréal. 679
Desjardins, G., Courville, A. C., Bengio, Y., Vincent, P., and Delalleau, O. (2010).
Tempered Markov chain Monte Carlo for training of restricted Boltzmann machines. In
International Conference on Artificial Intelligence and Statistics, pages 145–152. 601,
Desjardins, G., Courville, A., and Bengio, Y. (2011). On tracking the partition function.
In NIPS’2011 . 628
Desjardins, G., Simonyan, K., Pascanu, R., et al. (2015). Natural neural networks. In
Advances in Neural Information Processing Systems, pages 2062–2070. 316
Devlin, J., Zbib, R., Huang, Z., Lamar, T., Schwartz, R., and Makhoul, J. (2014). Fast
and robust neural network joint models for statistical machine translation. In Proc.
ACL’2014 . 468
Devroye, L. (2013). Non-Uniform Random Variate Generation. SpringerLink : Bücher.
Springer New York. 690
DiCarlo, J. J. (2013). Mechanisms underlying visual object recognition: Humans vs.
neurons vs. machines. NIPS Tutorial. 25, 360
Dinh, L., Krueger, D., and Bengio, Y. (2014). NICE: Non-linear independent components
estimation. arXiv:1410.8516. 489
Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko,
K., and Darrell, T. (2014). Long-term recurrent convolutional networks for visual
recognition and description. arXiv:1411.4389. 100
Donoho, D. L. and Grimes, C. (2003). Hessian eigenmaps: new locally linear embedding
techniques for high-dimensional data. Technical Report 2003-08, Dept. Statistics,
Stanford University. 160, 516
Dosovitskiy, A., Springenberg, J. T., and Brox, T. (2015). Learning to generate chairs with
convolutional neural networks. In Proceedings of the IEEE Conference on Computer
Vision and Pattern Recognition, pages 1538–1546. 692, 701
Doya, K. (1993). Bifurcations of recurrent neural networks in gradient descent learning.
IEEE Transactions on Neural Networks, 1, 75–80. 396, 399
Dreyfus, S. E. (1962). The numerical solution of variational problems. Journal of
Mathematical Analysis and Applications, 5(1), 30–45. 221
Dreyfus, S. E. (1973). The computational solution of optimal control problems with time
lag. IEEE Transactions on Automatic Control, 18(4), 383–385. 221
Drucker, H. and LeCun, Y. (1992). Improving generalisation performance using double
back-propagation. IEEE Transactions on Neural Networks, 3(6), 991–997. 269
Duchi, J., Hazan, E., and Singer, Y. (2011). Adaptive subgradient methods for online
learning and stochastic optimization. Journal of Machine Learning Research. 303
Dudik, M., Langford, J., and Li, L. (2011). Doubly robust policy evaluation and learning.
In Proceedings of the 28th International Conference on Machine learning, ICML ’11.
Dugas, C., Bengio, Y., Bélisle, F., and Nadeau, C. (2001). Incorporating second-order
functional knowledge for better option pricing. In T. Leen, T. Dietterich, and V. Tresp,
editors, Advances in Neural Information Processing Systems 13 (NIPS’00), pages
472–478. MIT Press. 66, 193
Dziugaite, G. K., Roy, D. M., and Ghahramani, Z. (2015). Training generative neural net-
works via maximum mean discrepancy optimization. arXiv preprint arXiv:1505.03906 .
El Hihi, S. and Bengio, Y. (1996). Hierarchical recurrent neural networks for long-term
dependencies. In NIPS’1995 . 394, 403
Elkahky, A. M., Song, Y., and He, X. (2015). A multi-view deep learning approach for
cross domain user modeling in recommendation systems. In Proceedings of the 24th
International Conference on World Wide Web, pages 278–288. 475
Elman, J. L. (1993). Learning and development in neural networks: The importance of
starting small. Cognition, 48, 781–799. 324
Erhan, D., Manzagol, P.-A., Bengio, Y., Bengio, S., and Vincent, P. (2009). The difficulty
of training deep architectures and the effect of unsupervised pre-training. In Proceedings
of AISTATS’2009 . 197
Erhan, D., Bengio, Y., Courville, A., Manzagol, P., Vincent, P., and Bengio, S. (2010).
Why does unsupervised pre-training help deep learning? J. Machine Learning Res.
527, 531, 532
Fahlman, S. E., Hinton, G. E., and Sejnowski, T. J. (1983). Massively parallel architectures
for AI: NETL, thistle, and Boltzmann machines. In Proceedings of the National
Conference on Artificial Intelligence AAAI-83 . 567, 651
Fang, H., Gupta, S., Iandola, F., Srivastava, R., Deng, L., Dollár, P., Gao, J., He, X.,
Mitchell, M., Platt, J. C., Zitnick, C. L., and Zweig, G. (2015). From captions to visual
concepts and back. arXiv:1411.4952. 100
Farabet, C., LeCun, Y., Kavukcuoglu, K., Culurciello, E., Martini, B., Akselrod, P., and
Talay, S. (2011). Large-scale FPGA-based convolutional networks. In R. Bekkerman,
M. Bilenko, and J. Langford, editors, Scaling up Machine Learning: Parallel and
Distributed Approaches. Cambridge University Press. 521
Farabet, C., Couprie, C., Najman, L., and LeCun, Y. (2013). Learning hierarchical features
for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence,
35(8), 1915–1929. 24, 197, 353
Fei-Fei, L., Fergus, R., and Perona, P. (2006). One-shot learning of object categories.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
(4), 594–611. 536
Finn, C., Tan, X. Y., Duan, Y., Darrell, T., Levine, S., and Abbeel, P. (2015). Learning
visual feature spaces for robotic manipulation with deep spatial autoencoders. arXiv
preprint arXiv:1509.06113 . 25
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals
of Eugenics, 7, 179–188. 19, 103
Földiák, P. (1989). Adaptive network for optimal linear feature extraction. In International
Joint Conference on Neural Networks (IJCNN), volume 1, pages 401–405, Washington
1989. IEEE, New York. 490
Forcada, M. and Ñeco, R. (1997). Learning recursive distributed representations for
holistic computation. In Biological and Artificial Computation: From Neuroscience to
Technology, pages 453–462. 468
Franzius, M., Sprekeler, H., and Wiskott, L. (2007). Slowness and sparseness lead to place,
head-direction, and spatial-view cells. 491
Franzius, M., Wilbert, N., and Wiskott, L. (2008). Invariant object recognition with slow
feature analysis. In Artificial Neural Networks-ICANN 2008 , pages 961–970. Springer.
Frasconi, P., Gori, M., and Sperduti, A. (1997). On the efficient classification of data
structures by neural networks. In Proc. Int. Joint Conf. on Artificial Intelligence. 394,
Frasconi, P., Gori, M., and Sperduti, A. (1998). A general framework for adaptive
processing of data structures. IEEE Transactions on Neural Networks,
(5), 768–786.
394, 396
Freund, Y. and Schapire, R. E. (1996a). Experiments with a new boosting algorithm. In
Machine Learning: Proceedings of Thirteenth International Conference, pages 148–156,
USA. ACM. 255
Freund, Y. and Schapire, R. E. (1996b). Game theory, on-line prediction and boosting. In
Proceedings of the Ninth Annual Conference on Computational Learning Theory, pages
325–332. 255
Frey, B. J. (1998). Graphical models for machine learning and digital communication.
MIT Press. 702
Frey, B. J., Hinton, G. E., and Dayan, P. (1996). Does the wake-sleep algorithm learn good
density estimators? In D. Touretzky, M. Mozer, and M. Hasselmo, editors, Advances
in Neural Information Processing Systems 8 (NIPS’95), pages 661–670. MIT Press,
Cambridge, MA. 649
Frobenius, G. (1908). Über matrizen aus positiven elementen, s. B. Preuss. Akad. Wiss.
Berlin, Germany. 594
Fukushima, K. (1975). Cognitron: A self-organizing multilayered neural network. Biological
Cybernetics, 20, 121–136. 15, 222, 526
Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a
mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics,
36, 193–202. 15, 22, 23, 222, 361
Gal, Y. and Ghahramani, Z. (2015). Bayesian convolutional neural networks with Bernoulli
approximate variational inference. arXiv preprint arXiv:1506.02158 . 261
Gallinari, P., LeCun, Y., Thiria, S., and Fogelman-Soulie, F. (1987). Memoires associatives
distribuees. In Proceedings of COGNITIVA 87 , Paris, La Villette. 511
Garcia-Duran, A., Bordes, A., Usunier, N., and Grandvalet, Y. (2015). Combining two
and three-way embeddings models for link prediction in knowledge bases. arXiv preprint
arXiv:1506.00999 . 479
Garofolo, J. S., Lamel, L. F., Fisher, W. M., Fiscus, J. G., and Pallett, D. S. (1993).
Darpa timit acoustic-phonetic continous speech corpus cd-rom. nist speech disc 1-1.1.
NASA STI/Recon Technical Report N , 93, 27403. 454
Garson, J. (1900). The metric system of identification of criminals, as used in Great
Britain and Ireland. The Journal of the Anthropological Institute of Great Britain and
Ireland, (2), 177–227. 19
Gers, F. A., Schmidhuber, J., and Cummins, F. (2000). Learning to forget: Continual
prediction with LSTM. Neural computation, 12(10), 2451–2471. 404, 408
Ghahramani, Z. and Hinton, G. E. (1996). The EM algorithm for mixtures of factor
analyzers. Technical Report CRG-TR-96-1, Dpt. of Comp. Sci., Univ. of Toronto. 485
Gillick, D., Brunk, C., Vinyals, O., and Subramanya, A. (2015). Multilingual language
processing from bytes. arXiv preprint arXiv:1512.00103 . 472
Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2015). Region-based convolutional
networks for accurate object detection and segmentation. 421
Giudice, M. D., Manera, V., and Keysers, C. (2009). Programmed to learn? The ontogeny
of mirror neurons. Dev. Sci., 12(2), 350––363. 653
Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward
neural networks. In AISTATS’2010 . 299
Glorot, X., Bordes, A., and Bengio, Y. (2011a). Deep sparse rectifier neural networks. In
AISTATS’2011 . 15, 171, 193, 222
Glorot, X., Bordes, A., and Bengio, Y. (2011b). Domain adaptation for large-scale
sentiment classification: A deep learning approach. In ICML’2011 . 504, 535
Goldberger, J., Roweis, S., Hinton, G. E., and Salakhutdinov, R. (2005). Neighbourhood
components analysis. In L. Saul, Y. Weiss, and L. Bottou, editors, Advances in Neural
Information Processing Systems 17 (NIPS’04). MIT Press. 113
Gong, S., McKenna, S., and Psarrou, A. (2000). Dynamic Vision: From Images to Face
Recognition. Imperial College Press. 161, 516
Goodfellow, I., Le, Q., Saxe, A., and Ng, A. (2009). Measuring invariances in deep
networks. In NIPS’2009 , pages 646–654. 252
Goodfellow, I., Koenig, N., Muja, M., Pantofaru, C., Sorokin, A., and Takayama, L. (2010).
Help me help you: Interfaces for personal robots. In Proc. of Human Robot Interaction
(HRI), Osaka, Japan. ACM Press, ACM Press. 98
Goodfellow, I. J. (2010). Technical report: Multidimensional, downsampled convolution
for autoencoders. Technical report, Université de Montréal. 350
Goodfellow, I. J. (2014). On distinguishability criteria for estimating generative models.
In International Conference on Learning Representations, Workshops Track. 620, 697
Goodfellow, I. J., Courville, A., and Bengio, Y. (2011). Spike-and-slab sparse coding
for unsupervised feature discovery. In NIPS Workshop on Challenges in Learning
Hierarchical Models. 530, 536
Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., and Bengio, Y. (2013a).
Maxout networks. In S. Dasgupta and D. McAllester, editors, ICML’13 , pages 1319–
1327. 190, 261, 338, 359, 450
Goodfellow, I. J., Mirza, M., Courville, A., and Bengio, Y. (2013b). Multi-prediction deep
Boltzmann machines. In NIPS26 . NIPS Foundation. 98, 615, 668, 669, 670, 671, 672,
Goodfellow, I. J., Warde-Farley, D., Lamblin, P., Dumoulin, V., Mirza, M., Pascanu, R.,
Bergstra, J., Bastien, F., and Bengio, Y. (2013c). Pylearn2: a machine learning research
library. arXiv preprint arXiv:1308.4214 . 25, 441
Goodfellow, I. J., Courville, A., and Bengio, Y. (2013d). Scaling up spike-and-slab models
for unsupervised feature learning. IEEE Transactions on Pattern Analysis and Machine
Intelligence, 35(8), 1902–1914. 493, 494, 495, 647, 679
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A., and Bengio, Y. (2014a). An empirical
investigation of catastrophic forgeting in gradient-based neural networks. In ICLR’2014 .
Goodfellow, I. J., Shlens, J., and Szegedy, C. (2014b). Explaining and harnessing adver-
sarial examples. CoRR, abs/1412.6572. 265, 266, 269, 553, 554
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S.,
Courville, A., and Bengio, Y. (2014c). Generative adversarial networks. In NIPS’2014 .
542, 685, 696, 698, 701
Goodfellow, I. J., Bulatov, Y., Ibarz, J., Arnoud, S., and Shet, V. (2014d). Multi-digit
number recognition from Street View imagery using deep convolutional neural networks.
In International Conference on Learning Representations. 25, 99, 197, 198, 199, 385,
417, 444
Goodfellow, I. J., Vinyals, O., and Saxe, A. M. (2015). Qualitatively characterizing neural
network optimization problems. In International Conference on Learning Representa-
tions. 282, 283, 284, 287
Goodman, J. (2001). Classes for fast maximum entropy training. In International
Conference on Acoustics, Speech and Signal Processing (ICASSP), Utah. 462
Gori, M. and Tesi, A. (1992). On the problem of local minima in backpropagation. IEEE
Transactions on Pattern Analysis and Machine Intelligence,
(1), 76–86. 282
Gosset, W. S. (1908). The probable error of a mean. Biometrika,
(1), 1–25. Originally
published under the pseudonym “Student”. 19
Gouws, S., Bengio, Y., and Corrado, G. (2014). BilBOWA: Fast bilingual distributed
representations without word alignments. Technical report, arXiv:1410.2455. 472, 537
Graf, H. P. and Jackel, L. D. (1989). Analog electronic neural network circuits. Circuits
and Devices Magazine, IEEE , 5(4), 44–49. 446
Graves, A. (2011). Practical variational inference for neural networks. In NIPS’2011 . 238
Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks. Studies
in Computational Intelligence. Springer. 368, 388, 407, 455
Graves, A. (2013). Generating sequences with recurrent neural networks. Technical report,
arXiv:1308.0850. 186, 404, 411, 415
Graves, A. and Jaitly, N. (2014). Towards end-to-end speech recognition with recurrent
neural networks. In ICML’2014 . 404
Graves, A. and Schmidhuber, J. (2005). Framewise phoneme classification with bidirec-
tional LSTM and other neural network architectures. Neural Networks,
(5), 602–610.
Graves, A. and Schmidhuber, J. (2009). Offline handwriting recognition with multidi-
mensional recurrent neural networks. In D. Koller, D. Schuurmans, Y. Bengio, and
L. Bottou, editors, NIPS’2008 , pages 545–552. 388
Graves, A., Fernández, S., Gomez, F., and Schmidhuber, J. (2006). Connectionist temporal
classification: Labelling unsegmented sequence data with recurrent neural networks. In
ICML’2006 , pages 369–376, Pittsburgh, USA. 455
Graves, A., Liwicki, M., Bunke, H., Schmidhuber, J., and Fernández, S. (2008). Uncon-
strained on-line handwriting recognition with recurrent neural networks. In J. Platt,
D. Koller, Y. Singer, and S. Roweis, editors, NIPS’2007 , pages 577–584. 388
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., and Schmidhuber, J.
(2009). A novel connectionist system for unconstrained handwriting recognition. Pattern
Analysis and Machine Intelligence, IEEE Transactions on, 31(5), 855–868. 404
Graves, A., Mohamed, A., and Hinton, G. (2013). Speech recognition with deep recurrent
neural networks. In ICASSP’2013 , pages 6645–6649. 388, 393, 394, 404, 406, 407, 455
Graves, A., Wayne, G., and Danihelka, I. (2014a). Neural Turing machines.
arXiv:1410.5401. 25
Graves, A., Wayne, G., and Danihelka, I. (2014b). Neural Turing machines. arXiv preprint
arXiv:1410.5401 . 412
Grefenstette, E., Hermann, K. M., Suleyman, M., and Blunsom, P. (2015). Learning to
transduce with unbounded memory. In NIPS’2015 . 412
Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., and Schmidhuber, J. (2015).
LSTM: a search space odyssey. arXiv preprint arXiv:1503.04069 . 408
Gregor, K. and LeCun, Y. (2010a). Emergence of complex-like cells in a temporal product
network with local receptive fields. Technical report, arXiv:1006.0448. 346
Gregor, K. and LeCun, Y. (2010b). Learning fast approximations of sparse coding. In
L. Bottou and M. Littman, editors, Proceedings of the Twenty-seventh International
Conference on Machine Learning (ICML-10). ACM. 650
Gregor, K., Danihelka, I., Mnih, A., Blundell, C., and Wierstra, D. (2014). Deep
autoregressive networks. In International Conference on Machine Learning (ICML’2014).
Gregor, K., Danihelka, I., Graves, A., and Wierstra, D. (2015). DRAW: A recurrent neural
network for image generation. arXiv preprint arXiv:1502.04623 . 694
Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012). A
kernel two-sample test. The Journal of Machine Learning Research,
(1), 723–773.
Gülçehre, Ç. and Bengio, Y. (2013). Knowledge matters: Importance of prior information
for optimization. In International Conference on Learning Representations (ICLR’2013).
Guo, H. and Gelfand, S. B. (1992). Classification trees with neural network feature
extraction. Neural Networks, IEEE Transactions on, 3(6), 923–933. 445
Gupta, S., Agrawal, A., Gopalakrishnan, K., and Narayanan, P. (2015). Deep learning
with limited numerical precision. CoRR, abs/1502.02551. 447
Gutmann, M. and Hyvarinen, A. (2010). Noise-contrastive estimation: A new estima-
tion principle for unnormalized statistical models. In Proceedings of The Thirteenth
International Conference on Artificial Intelligence and Statistics (AISTATS’10). 618
Hadsell, R., Sermanet, P., Ben, J., Erkan, A., Han, J., Muller, U., and LeCun, Y.
(2007). Online learning for offroad robots: Spatial label propagation to learn long-range
traversability. In Proceedings of Robotics: Science and Systems, Atlanta, GA, USA. 448
Hajnal, A., Maass, W., Pudlak, P., Szegedy, M., and Turan, G. (1993). Threshold circuits
of bounded depth. J. Comput. System. Sci., 46, 129–154. 196
Håstad, J. (1986). Almost optimal lower bounds for small depth circuits. In Proceedings
of the 18th annual ACM Symposium on Theory of Computing, pages 6–20, Berkeley,
California. ACM Press. 196
Håstad, J. and Goldmann, M. (1991). On the power of small-depth threshold circuits.
Computational Complexity, 1, 113–129. 196
Hastie, T., Tibshirani, R., and Friedman, J. (2001). The elements of statistical learning:
data mining, inference and prediction. Springer Series in Statistics. Springer Verlag.
He, K., Zhang, X., Ren, S., and Sun, J. (2015). Delving deep into rectifiers: Surpassing
human-level performance on ImageNet classification. arXiv preprint arXiv:1502.01852 .
24, 190
Hebb, D. O. (1949). The Organization of Behavior. Wiley, New York. 13, 16, 653
Henaff, M., Jarrett, K., Kavukcuoglu, K., and LeCun, Y. (2011). Unsupervised learning
of sparse features for scalable audio classification. In ISMIR’11 . 521
Henderson, J. (2003). Inducing history representations for broad coverage statistical
parsing. In HLT-NAACL, pages 103–110. 473
Henderson, J. (2004). Discriminative training of a neural network statistical parser. In
Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics,
page 95. 473
Henniges, M., Puertas, G., Bornschein, J., Eggert, J., and Lücke, J. (2010). Binary sparse
coding. In Latent Variable Analysis and Signal Separation, pages 450–457. Springer.
Herault, J. and Ans, B. (1984). Circuits neuronaux à synapses modifiables: Décodage de
messages composites par apprentissage non supervisé. Comptes Rendus de l’Académie
des Sciences, 299(III-13), 525––528. 487
Hinton, G. (2012). Neural networks for machine learning. Coursera, video lectures. 303
Hinton, G., Deng, L., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V.,
Nguyen, P., Sainath, T., and Kingsbury, B. (2012a). Deep neural networks for acoustic
modeling in speech recognition. IEEE Signal Processing Magazine,
(6), 82–97. 24,
Hinton, G., Vinyals, O., and Dean, J. (2015). Distilling the knowledge in a neural network.
arXiv preprint arXiv:1503.02531 . 443
Hinton, G. E. (1989). Connectionist learning procedures. Artificial Intelligence,
185–234. 490
Hinton, G. E. (1990). Mapping part-whole hierarchies into connectionist networks. Artificial
Intelligence, 46(1), 47–75. 412
Hinton, G. E. (1999). Products of experts. In ICANN’1999 . 567
Hinton, G. E. (2000). Training products of experts by minimizing contrastive divergence.
Technical Report GCNU TR 2000-004, Gatsby Unit, University College London. 608,
Hinton, G. E. (2006). To recognize shapes, first learn to generate images. Technical Report
UTML TR 2006-003, University of Toronto. 526, 592
Hinton, G. E. (2007a). How to do backpropagation in a brain. Invited talk at the
NIPS’2007 Deep Learning Workshop. 653
Hinton, G. E. (2007b). Learning multiple layers of representation. Trends in cognitive
sciences, 11(10), 428–434. 657
Hinton, G. E. (2010). A practical guide to training restricted Boltzmann machines.
Technical Report UTML TR 2010-003, Department of Computer Science, University of
Toronto. 608
Hinton, G. E. and Ghahramani, Z. (1997). Generative models for discovering sparse
distributed representations. Philosophical Transactions of the Royal Society of London.
Hinton, G. E. and McClelland, J. L. (1988). Learning representations by recirculation. In
NIPS’1987 , pages 358–366. 499
Hinton, G. E. and Roweis, S. (2003). Stochastic neighbor embedding. In NIPS’2002 . 516
Hinton, G. E. and Salakhutdinov, R. (2006). Reducing the dimensionality of data with
neural networks. Science, 313(5786), 504–507. 506, 522, 526, 527, 531
Hinton, G. E. and Sejnowski, T. J. (1986). Learning and relearning in Boltzmann machines.
In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing,
volume 1, chapter 7, pages 282–317. MIT Press, Cambridge. 567, 651
Hinton, G. E. and Sejnowski, T. J. (1999). Unsupervised learning: foundations of neural
computation. MIT press. 539
Hinton, G. E. and Shallice, T. (1991). Lesioning an attractor network: investigations of
acquired dyslexia. Psychological review, 98(1), 74. 13
Hinton, G. E. and Zemel, R. S. (1994). Autoencoders, minimum description length, and
Helmholtz free energy. In NIPS’1993 . 499
Hinton, G. E., Sejnowski, T. J., and Ackley, D. H. (1984). Boltzmann machines: Constraint
satisfaction networks that learn. Technical Report TR-CMU-CS-84-119, Carnegie-Mellon
University, Dept. of Computer Science. 567, 651
Hinton, G. E., McClelland, J., and Rumelhart, D. (1986). Distributed representations.
In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing:
Explorations in the Microstructure of Cognition, volume 1, pages 77–109. MIT Press,
Cambridge. 16, 221, 524
Hinton, G. E., Revow, M., and Dayan, P. (1995a). Recognizing handwritten digits using
mixtures of linear models. In G. Tesauro, D. Touretzky, and T. Leen, editors, Advances
in Neural Information Processing Systems 7 (NIPS’94), pages 1015–1022. MIT Press,
Cambridge, MA. 485
Hinton, G. E., Dayan, P., Frey, B. J., and Neal, R. M. (1995b). The wake-sleep algorithm
for unsupervised neural networks. Science, 268, 1558–1161. 501, 649
Hinton, G. E., Dayan, P., and Revow, M. (1997). Modelling the manifolds of images of
handwritten digits. IEEE Transactions on Neural Networks, 8, 65–74. 496
Hinton, G. E., Welling, M., Teh, Y. W., and Osindero, S. (2001). A new view of ICA. In
Proceedings of 3rd International Conference on Independent Component Analysis and
Blind Signal Separation (ICA’01), pages 746–751, San Diego, CA. 487
Hinton, G. E., Osindero, S., and Teh, Y. (2006). A fast learning algorithm for deep belief
nets. Neural Computation, 18, 1527–1554. 13, 18, 23, 141, 526, 527, 657, 658
Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A.,
Vanhoucke, V., Nguyen, P., Sainath, T. N., and Kingsbury, B. (2012b). Deep neural
networks for acoustic modeling in speech recognition: The shared views of four research
groups. IEEE Signal Process. Mag., 29(6), 82–97. 99
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. (2012c).
Improving neural networks by preventing co-adaptation of feature detectors. Technical
report, arXiv:1207.0580. 235, 259, 264
Hinton, G. E., Vinyals, O., and Dean, J. (2014). Dark knowledge. Invited talk at the
BayLearn Bay Area Machine Learning Symposium. 443
Hochreiter, S. (1991). Untersuchungen zu dynamischen neuronalen Netzen. Diploma
thesis, T.U. München. 17, 396, 398
Hochreiter, S. and Schmidhuber, J. (1995). Simplifying neural nets by discovering flat
minima. In Advances in Neural Information Processing Systems 7 , pages 529–536. MIT
Press. 239
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural Computation,
9(8), 1735–1780. 17, 404, 407
Hochreiter, S., Bengio, Y., and Frasconi, P. (2001). Gradient flow in recurrent nets: the
difficulty of learning long-term dependencies. In J. Kolen and S. Kremer, editors, Field
Guide to Dynamical Recurrent Networks. IEEE Press. 407
Holi, J. L. and Hwang, J.-N. (1993). Finite precision error analysis of neural network
hardware implementations. Computers, IEEE Transactions on, 42(3), 281–290. 446
Holt, J. L. and Baker, T. E. (1991). Back propagation simulations using limited preci-
sion calculations. In Neural Networks, 1991., IJCNN-91-Seattle International Joint
Conference on, volume 2, pages 121–126. IEEE. 446
Hornik, K., Stinchcombe, M., and White, H. (1989). Multilayer feedforward networks are
universal approximators. Neural Networks, 2, 359–366. 194
Hornik, K., Stinchcombe, M., and White, H. (1990). Universal approximation of an
unknown mapping and its derivatives using multilayer feedforward networks. Neural
networks, 3(5), 551–560. 194
Hsu, F.-H. (2002). Behind Deep Blue: Building the Computer That Defeated the World
Chess Champion. Princeton University Press, Princeton, NJ, USA. 2
Huang, F. and Ogata, Y. (2002). Generalized pseudo-likelihood estimates for Markov
random fields on lattice. Annals of the Institute of Statistical Mathematics,
(1), 1–18.
Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., and Heck, L. (2013). Learning deep
structured semantic models for web search using clickthrough data. In Proceedings of
the 22nd ACM international conference on Conference on information & knowledge
management, pages 2333–2338. ACM. 475
Hubel, D. and Wiesel, T. (1968). Receptive fields and functional architecture of monkey
striate cortex. Journal of Physiology (London), 195, 215–243. 358
Hubel, D. H. and Wiesel, T. N. (1959). Receptive fields of single neurons in the cat’s
striate cortex. Journal of Physiology, 148, 574–591. 358
Hubel, D. H. and Wiesel, T. N. (1962). Receptive fields, binocular interaction, and
functional architecture in the cat’s visual cortex. Journal of Physiology (London),
106–154. 358
Huszar, F. (2015). How (not) to train your generative model: schedule sampling, likelihood,
adversary? arXiv:1511.05101. 694
Hutter, F., Hoos, H., and Leyton-Brown, K. (2011). Sequential model-based optimization
for general algorithm configuration. In LION-5 . Extended version as UBC Tech report
TR-2010-10. 430
Hyotyniemi, H. (1996). Turing machines are recurrent neural networks. In STeP’96 , pages
13–24. 372
Hyvärinen, A. (1999). Survey on independent component analysis. Neural Computing
Surveys, 2, 94–128. 487
Hyvärinen, A. (2005). Estimation of non-normalized statistical models using score matching.
Journal of Machine Learning Research, 6, 695–709. 509, 615
Hyvärinen, A. (2007a). Connections between score matching, contrastive divergence,
and pseudolikelihood for continuous-valued variables. IEEE Transactions on Neural
Networks, 18, 1529–1531. 616
Hyvärinen, A. (2007b). Some extensions of score matching. Computational Statistics and
Data Analysis, 51, 2499–2512. 616
Hyvärinen, A. and Hoyer, P. O. (1999). Emergence of topography and complex cell
properties from natural images using extensions of ica. In NIPS , pages 827–833. 489
Hyvärinen, A. and Pajunen, P. (1999). Nonlinear independent component analysis:
Existence and uniqueness results. Neural Networks, 12(3), 429–439. 489
Hyvärinen, A., Karhunen, J., and Oja, E. (2001a). Independent Component Analysis.
Wiley-Interscience. 487
Hyvärinen, A., Hoyer, P. O., and Inki, M. O. (2001b). Topographic independent component
analysis. Neural Computation, 13(7), 1527–1558. 489
Hyvärinen, A., Hurri, J., and Hoyer, P. O. (2009). Natural Image Statistics: A probabilistic
approach to early computational vision. Springer-Verlag. 364
Iba, Y. (2001). Extended ensemble Monte Carlo. International Journal of Modern Physics,
C12, 623–656. 601
Inayoshi, H. and Kurita, T. (2005). Improved generalization by adding both auto-
association and hidden-layer noise to neural-network-based-classifiers. IEEE Workshop
on Machine Learning for Signal Processing, pages 141—-146. 511
Ioffe, S. and Szegedy, C. (2015). Batch normalization: Accelerating deep network training
by reducing internal covariate shift. 98, 313, 316
Jacobs, R. A. (1988). Increased rates of convergence through learning rate adaptation.
Neural networks, 1(4), 295–307. 303
Jacobs, R. A., Jordan, M. I., Nowlan, S. J., and Hinton, G. E. (1991). Adaptive mixtures
of local experts. Neural Computation, 3, 79–87. 185, 445
Jaeger, H. (2003). Adaptive nonlinear system identification with echo state networks. In
Advances in Neural Information Processing Systems 15 . 399
Jaeger, H. (2007a). Discovering multiscale dynamical features with hierarchical echo state
networks. Technical report, Jacobs University. 394
Jaeger, H. (2007b). Echo state network. Scholarpedia, 2(9), 2330. 399
Jaeger, H. (2012). Long short-term memory in echo state networks: Details of a simulation
study. Technical report, Technical report, Jacobs University Bremen. 400
Jaeger, H. and Haas, H. (2004). Harnessing nonlinearity: Predicting chaotic systems and
saving energy in wireless communication. Science, 304(5667), 78–80. 23, 399
Jaeger, H., Lukosevicius, M., Popovici, D., and Siewert, U. (2007). Optimization and
applications of echo state networks with leaky- integrator neurons. Neural Networks,
20(3), 335–352. 403
Jain, V., Murray, J. F., Roth, F., Turaga, S., Zhigulin, V., Briggman, K. L., Helmstaedter,
M. N., Denk, W., and Seung, H. S. (2007). Supervised learning of image restoration
with convolutional networks. In Computer Vision, 2007. ICCV 2007. IEEE 11th
International Conference on, pages 1–8. IEEE. 352
Jaitly, N. and Hinton, G. (2011). Learning a better representation of speech soundwaves
using restricted Boltzmann machines. In Acoustics, Speech and Signal Processing
(ICASSP), 2011 IEEE International Conference on, pages 5884–5887. IEEE. 453
Jaitly, N. and Hinton, G. E. (2013). Vocal tract length perturbation (VTLP) improves
speech recognition. In ICML’2013 . 237
Jarrett, K., Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2009). What is the best
multi-stage architecture for object recognition? In ICCV’09 . 15, 22, 23, 171, 190, 222,
357, 358, 521
Jarzynski, C. (1997). Nonequilibrium equality for free energy differences. Phys. Rev. Lett.,
78, 2690–2693. 623, 626
Jaynes, E. T. (2003). Probability Theory: The Logic of Science. Cambridge University
Press. 51
Jean, S., Cho, K., Memisevic, R., and Bengio, Y. (2014). On using very large target
vocabulary for neural machine translation. arXiv:1412.2007. 469, 470
Jelinek, F. and Mercer, R. L. (1980). Interpolated estimation of Markov source parameters
from sparse data. In E. S. Gelsema and L. N. Kanal, editors, Pattern Recognition in
Practice. North-Holland, Amsterdam. 457, 468
Jia, Y. (2013). Caffe: An open source convolutional architecture for fast feature embedding. 25, 209
Jia, Y., Huang, C., and Darrell, T. (2012). Beyond spatial pyramids: Receptive field
learning for pooled image features. In Computer Vision and Pattern Recognition
(CVPR), 2012 IEEE Conference on, pages 3370–3377. IEEE. 339
Jim, K.-C., Giles, C. L., and Horne, B. G. (1996). An analysis of noise in recurrent neural
networks: convergence and generalization. IEEE Transactions on Neural Networks,
7(6), 1424–1438. 238
Jordan, M. I. (1998). Learning in Graphical Models. Kluwer, Dordrecht, Netherlands. 17
Joulin, A. and Mikolov, T. (2015). Inferring algorithmic patterns with stack-augmented
recurrent nets. arXiv preprint arXiv:1503.01007 . 412
Jozefowicz, R., Zaremba, W., and Sutskever, I. (2015). An empirical evaluation of recurrent
network architectures. In ICML’2015 . 302, 407, 408
Judd, J. S. (1989). Neural Network Design and the Complexity of Learning. MIT press.
Jutten, C. and Herault, J. (1991). Blind separation of sources, part I: an adaptive
algorithm based on neuromimetic architecture. Signal Processing, 24, 1–10. 487
Kahou, S. E., Pal, C., Bouthillier, X., Froumenty, P., Gülçehre, c., Memisevic, R., Vincent,
P., Courville, A., Bengio, Y., Ferrari, R. C., Mirza, M., Jean, S., Carrier, P. L., Dauphin,
Y., Boulanger-Lewandowski, N., Aggarwal, A., Zumer, J., Lamblin, P., Raymond,
J.-P., Desjardins, G., Pascanu, R., Warde-Farley, D., Torabi, A., Sharma, A., Bengio,
E., Côté, M., Konda, K. R., and Wu, Z. (2013). Combining modality specific deep
neural networks for emotion recognition in video. In Proceedings of the 15th ACM on
International Conference on Multimodal Interaction. 197
Kalchbrenner, N. and Blunsom, P. (2013). Recurrent continuous translation models. In
EMNLP’2013 . 469, 470
Kalchbrenner, N., Danihelka, I., and Graves, A. (2015). Grid long short-term memory.
arXiv preprint arXiv:1507.01526 . 390
Kamyshanska, H. and Memisevic, R. (2015). The potential energy of an autoencoder.
IEEE Transactions on Pattern Analysis and Machine Intelligence. 511
Karpathy, A. and Li, F.-F. (2015). Deep visual-semantic alignments for generating image
descriptions. In CVPR’2015 . arXiv:1412.2306. 100
Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., and Fei-Fei, L. (2014).
Large-scale video classification with convolutional neural networks. In CVPR. 19
Karush, W. (1939). Minima of Functions of Several Variables with Inequalities as Side
Constraints. Master’s thesis, Dept. of Mathematics, Univ. of Chicago. 93
Katz, S. M. (1987). Estimation of probabilities from sparse data for the language model
component of a speech recognizer. IEEE Transactions on Acoustics, Speech, and Signal
Processing, ASSP-35(3), 400–401. 457, 468
Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2008). Fast inference in sparse coding
algorithms with applications to object recognition. Technical report, Computational and
Biological Learning Lab, Courant Institute, NYU. Tech Report CBLL-TR-2008-12-01.
Kavukcuoglu, K., Ranzato, M.-A., Fergus, R., and LeCun, Y. (2009). Learning invariant
features through topographic filter maps. In CVPR’2009 . 521
Kavukcuoglu, K., Sermanet, P., Boureau, Y.-L., Gregor, K., Mathieu, M., and LeCun, Y.
(2010). Learning convolutional feature hierarchies for visual recognition. In NIPS’2010 .
358, 521
Kelley, H. J. (1960). Gradient theory of optimal flight paths. ARS Journal,
947–954. 221
Khan, F., Zhu, X., and Mutlu, B. (2011). How do humans teach: On curriculum learning
and teaching dimension. In Advances in Neural Information Processing Systems 24
(NIPS’11), pages 1449–1457. 324
Kim, S. K., McAfee, L. C., McMahon, P. L., and Olukotun, K. (2009). A highly scalable
restricted Boltzmann machine FPGA implementation. In Field Programmable Logic
and Applications, 2009. FPL 2009. International Conference on, pages 367–372. IEEE.
Kindermann, R. (1980). Markov Random Fields and Their Applications (Contemporary
Mathematics ; V. 1). American Mathematical Society. 563
Kingma, D. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv
preprint arXiv:1412.6980 . 305
Kingma, D. and LeCun, Y. (2010). Regularized estimation of image statistics by score
matching. In NIPS’2010 . 509, 618
Kingma, D., Rezende, D., Mohamed, S., and Welling, M. (2014). Semi-supervised learning
with deep generative models. In NIPS’2014 . 421
Kingma, D. P. (2013). Fast gradient-based inference with continuous latent variable
models in auxiliary form. Technical report, arxiv:1306.0733. 650, 685, 693
Kingma, D. P. and Welling, M. (2014a). Auto-encoding variational bayes. In Proceedings
of the International Conference on Learning Representations (ICLR). 685, 696
Kingma, D. P. and Welling, M. (2014b). Efficient gradient-based inference through
transformations between bayes nets and neural nets. Technical report, arxiv:1402.0480.
Kirkpatrick, S., Jr., C. D. G., , and Vecchi, M. P. (1983). Optimization by simulated
annealing. Science, 220, 671–680. 323
Kiros, R., Salakhutdinov, R., and Zemel, R. (2014a). Multimodal neural language models.
In ICML’2014 . 100
Kiros, R., Salakhutdinov, R., and Zemel, R. (2014b). Unifying visual-semantic embeddings
with multimodal neural language models. arXiv:1411.2539 [cs.LG]. 100, 404
Klementiev, A., Titov, I., and Bhattarai, B. (2012). Inducing crosslingual distributed
representations of words. In Proceedings of COLING 2012 . 472, 537
Knowles-Barley, S., Jones, T. R., Morgan, J., Lee, D., Kasthuri, N., Lichtman, J. W., and
Pfister, H. (2014). Deep learning for the connectome. GPU Technology Conference. 26
Koller, D. and Friedman, N. (2009). Probabilistic Graphical Models: Principles and
Techniques. MIT Press. 580, 592, 643
Konig, Y., Bourlard, H., and Morgan, N. (1996). REMAP: Recursive estimation and
maximization of a posteriori probabilities application to transition-based connectionist
speech recognition. In D. Touretzky, M. Mozer, and M. Hasselmo, editors, Advances in
Neural Information Processing Systems 8 (NIPS’95). MIT Press, Cambridge, MA. 454
Koren, Y. (2009). The BellKor solution to the Netflix grand prize. 255, 475
Kotzias, D., Denil, M., de Freitas, N., and Smyth, P. (2015). From group to individual
labels using deep features. In ACM SIGKDD. 104
Koutnik, J., Greff, K., Gomez, F., and Schmidhuber, J. (2014). A clockwork RNN. In
ICML’2014 . 403
Kočiský, T., Hermann, K. M., and Blunsom, P. (2014). Learning Bilingual Word Repre-
sentations by Marginalizing Alignments. In Proceedings of ACL. 470
Krause, O., Fischer, A., Glasmachers, T., and Igel, C. (2013). Approximation properties
of DBNs with binary hidden units and real-valued visible units. In ICML’2013 . 551
Krizhevsky, A. (2010). Convolutional deep belief networks on CIFAR-10. Technical report,
University of Toronto. Unpublished Manuscript: kriz/conv-
cifar10-aug2010.pdf. 441
Krizhevsky, A. and Hinton, G. (2009). Learning multiple layers of features from tiny
images. Technical report, University of Toronto. 19, 558
Krizhevsky, A. and Hinton, G. E. (2011). Using very deep autoencoders for content-based
image retrieval. In ESANN . 523
Krizhevsky, A., Sutskever, I., and Hinton, G. (2012). ImageNet classification with deep
convolutional neural networks. In NIPS’2012 . 22, 23, 24, 98, 197, 365, 449, 453
Krueger, K. A. and Dayan, P. (2009). Flexible shaping: how learning in small steps helps.
Cognition, 110, 380–394. 324
Kuhn, H. W. and Tucker, A. W. (1951). Nonlinear programming. In Proceedings of the
Second Berkeley Symposium on Mathematical Statistics and Probability, pages 481–492,
Berkeley, Calif. University of California Press. 93
Kumar, A., Irsoy, O., Su, J., Bradbury, J., English, R., Pierce, B., Ondruska, P., Iyyer,
M., Gulrajani, I., and Socher, R. (2015). Ask me anything: Dynamic memory networks
for natural language processing. arXiv:1506.07285 . 412, 480
Kumar, M. P., Packer, B., and Koller, D. (2010). Self-paced learning for latent variable
models. In NIPS’2010 . 324
Lang, K. J. and Hinton, G. E. (1988). The development of the time-delay neural network
architecture for speech recognition. Technical Report CMU-CS-88-152, Carnegie-Mellon
University. 361, 368, 402
Lang, K. J., Waibel, A. H., and Hinton, G. E. (1990). A time-delay neural network
architecture for isolated word recognition. Neural networks, 3(1), 23–43. 368
Langford, J. and Zhang, T. (2008). The epoch-greedy algorithm for contextual multi-armed
bandits. In NIPS’2008 , pages 1096––1103. 476
Lappalainen, H., Giannakopoulos, X., Honkela, A., and Karhunen, J. (2000). Nonlinear
independent component analysis using ensemble learning: Experiments and discussion.
In Proc. ICA. Citeseer. 489
Larochelle, H. and Bengio, Y. (2008). Classification using discriminative restricted
Boltzmann machines. In ICML’2008 . 240, 252, 528, 683, 712
Larochelle, H. and Hinton, G. E. (2010). Learning to combine foveal glimpses with a
third-order Boltzmann machine. In Advances in Neural Information Processing Systems
23 , pages 1243–1251. 361
Larochelle, H. and Murray, I. (2011). The Neural Autoregressive Distribution Estimator.
In AISTATS’2011 . 702, 705, 706
Larochelle, H., Erhan, D., and Bengio, Y. (2008). Zero-data learning of new tasks. In
AAAI Conference on Artificial Intelligence. 537
Larochelle, H., Bengio, Y., Louradour, J., and Lamblin, P. (2009). Exploring strategies for
training deep neural networks. Journal of Machine Learning Research, 10, 1–40. 533
Lasserre, J. A., Bishop, C. M., and Minka, T. P. (2006). Principled hybrids of generative and
discriminative models. In Proceedings of the Computer Vision and Pattern Recognition
Conference (CVPR’06), pages 87–94, Washington, DC, USA. IEEE Computer Society.
240, 250
Le, Q., Ngiam, J., Chen, Z., hao Chia, D. J., Koh, P. W., and Ng, A. (2010). Tiled
convolutional neural networks. In J. Lafferty, C. K. I. Williams, J. Shawe-Taylor,
R. Zemel, and A. Culotta, editors, Advances in Neural Information Processing Systems
23 (NIPS’10), pages 1279–1287. 346
Le, Q., Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., and Ng, A. (2011). On optimization
methods for deep learning. In Proc. ICML’2011 . ACM. 312
Le, Q., Ranzato, M., Monga, R., Devin, M., Corrado, G., Chen, K., Dean, J., and Ng,
A. (2012). Building high-level features using large scale unsupervised learning. In
ICML’2012 . 22, 23
Le Roux, N. and Bengio, Y. (2008). Representational power of restricted Boltzmann
machines and deep belief networks. Neural Computation, 20(6), 1631–1649. 551, 652
Le Roux, N. and Bengio, Y. (2010). Deep belief networks are compact universal approxi-
mators. Neural Computation, 22(8), 2192–2207. 551
LeCun, Y. (1985). Une procédure d’apprentissage pour Réseau à seuil assymétrique. In
Cognitiva 85: A la Frontière de l’Intelligence Artificielle, des Sciences de la Connaissance
et des Neurosciences, pages 599–604, Paris 1985. CESTA, Paris. 221
LeCun, Y. (1986). Learning processes in an asymmetric threshold network. In F. Fogelman-
Soulié, E. Bienenstock, and G. Weisbuch, editors, Disordered Systems and Biological
Organization, pages 233–240. Springer-Verlag, Les Houches, France. 345
LeCun, Y. (1987). Modèles connexionistes de l’apprentissage. Ph.D. thesis, Université de
Paris VI. 17, 499, 511
LeCun, Y. (1989). Generalization and network design strategies. Technical Report
CRG-TR-89-4, University of Toronto. 326, 345
LeCun, Y., Jackel, L. D., Boser, B., Denker, J. S., Graf, H. P., Guyon, I., Henderson, D.,
Howard, R. E., and Hubbard, W. (1989). Handwritten digit recognition: Applications
of neural network chips and automatic learning. IEEE Communications Magazine,
27(11), 41–46. 362
LeCun, Y., Bottou, L., Orr, G. B., and Müller, K.-R. (1998a). Efficient backprop. In
Neural Networks, Tricks of the Trade, Lecture Notes in Computer Science LNCS 1524.
Springer Verlag. 307, 424
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998b). Gradient based learning
applied to document recognition. Proc. IEEE. 15, 17, 19, 23, 365, 453, 455
LeCun, Y., Kavukcuoglu, K., and Farabet, C. (2010). Convolutional networks and
applications in vision. In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE
International Symposium on, pages 253–256. IEEE. 365
L’Ecuyer, P. (1994). Efficiency improvement and variance reduction. In Proceedings of
the 1994 Winter Simulation Conference, pages 122––132. 687
Lee, C.-Y., Xie, S., Gallagher, P., Zhang, Z., and Tu, Z. (2014). Deeply-supervised nets.
arXiv preprint arXiv:1409.5185 . 322
Lee, H., Battle, A., Raina, R., and Ng, A. (2007). Efficient sparse coding algorithms.
In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information
Processing Systems 19 (NIPS’06), pages 801–808. MIT Press. 635
Lee, H., Ekanadham, C., and Ng, A. (2008). Sparse deep belief net model for visual area
V2. In NIPS’07 . 252
Lee, H., Grosse, R., Ranganath, R., and Ng, A. Y. (2009). Convolutional deep belief
networks for scalable unsupervised learning of hierarchical representations. In L. Bottou
and M. Littman, editors, Proceedings of the Twenty-sixth International Conference on
Machine Learning (ICML’09). ACM, Montreal, Canada. 357, 680, 681
Lee, Y. J. and Grauman, K. (2011). Learning the easy things first: self-paced visual
category discovery. In CVPR’2011 . 324
Leibniz, G. W. (1676). Memoir using the chain rule. (Cited in TMME 7:2&3 p 321-332,
2010). 220
Lenat, D. B. and Guha, R. V. (1989). Building large knowledge-based systems; representa-
tion and inference in the Cyc project. Addison-Wesley Longman Publishing Co., Inc.
Leshno, M., Lin, V. Y., Pinkus, A., and Schocken, S. (1993). Multilayer feedforward
networks with a nonpolynomial activation function can approximate any function.
Neural Networks, 6, 861––867. 195, 196
Levenberg, K. (1944). A method for the solution of certain non-linear problems in least
squares. Quarterly Journal of Applied Mathematics, II(2), 164–168. 308
L’Hôpital, G. F. A. (1696). Analyse des infiniment petits, pour l’intelligence des lignes
courbes. Paris: L’Imprimerie Royale. 220
Li, Y., Swersky, K., and Zemel, R. S. (2015). Generative moment matching networks.
CoRR, abs/1502.02761. 699
Lin, T., Horne, B. G., Tino, P., and Giles, C. L. (1996). Learning long-term dependencies
is not as difficult with NARX recurrent neural networks. IEEE Transactions on Neural
Networks, 7(6), 1329–1338. 402
Lin, Y., Liu, Z., Sun, M., Liu, Y., and Zhu, X. (2015). Learning entity and relation
embeddings for knowledge graph completion. In Proc. AAAI’15 . 479
Linde, N. (1992). The machine that changed the world, episode 3. Documentary miniseries.
Lindsey, C. and Lindblad, T. (1994). Review of hardware neural networks: a user’s
perspective. In Proc. Third Workshop on Neural Networks: From Biology to High
Energy Physics, pages 195––202, Isola d’Elba, Italy. 446
Linnainmaa, S. (1976). Taylor expansion of the accumulated rounding error. BIT
Numerical Mathematics, 16(2), 146–160. 221
Long, P. M. and Servedio, R. A. (2010). Restricted Boltzmann machines are hard to
approximately evaluate or simulate. In Proceedings of the 27th International Conference
on Machine Learning (ICML’10). 655
Lotter, W., Kreiman, G., and Cox, D. (2015). Unsupervised learning of visual structure
using predictive generative networks. arXiv preprint arXiv:1511.06380 . 542, 543
Lovelace, A. (1842). Notes upon L. F. Menabrea’s Sketch of the Analytical Engine
invented by Charles Babbage”. 1
Lu, L., Zhang, X., Cho, K., and Renals, S. (2015). A study of the recurrent neural network
encoder-decoder for large vocabulary speech recognition. In Proc. Interspeech. 455
Lu, T., Pál, D., and Pál, M. (2010). Contextual multi-armed bandits. In International
Conference on Artificial Intelligence and Statistics, pages 485–492. 476
Luenberger, D. G. (1984). Linear and Nonlinear Programming. Addison Wesley. 312
Lukoševičius, M. and Jaeger, H. (2009). Reservoir computing approaches to recurrent
neural network training. Computer Science Review, 3(3), 127–149. 399
Luo, H., Shen, R., Niu, C., and Ullrich, C. (2011). Learning class-relevant features and
class-irrelevant features via a hybrid third-order RBM. In International Conference on
Artificial Intelligence and Statistics, pages 470–478. 683
Luo, H., Carrier, P. L., Courville, A., and Bengio, Y. (2013). Texture modeling with
convolutional spike-and-slab RBMs and deep extensions. In AISTATS’2013 . 100
Lyu, S. (2009). Interpretation and generalization of score matching. In Proceedings of the
Twenty-fifth Conference in Uncertainty in Artificial Intelligence (UAI’09). 616
Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., and Svetnik, V. (2015). Deep neural nets
as a method for quantitative structure activity relationships. J. Chemical information
and modeling. 528
Maas, A. L., Hannun, A. Y., and Ng, A. Y. (2013). Rectifier nonlinearities improve neural
network acoustic models. In ICML Workshop on Deep Learning for Audio, Speech, and
Language Processing. 190
Maass, W. (1992). Bounds for the computational power and learning complexity of analog
neural nets (extended abstract). In Proc. of the 25th ACM Symp. Theory of Computing,
pages 335–344. 196
Maass, W., Schnitger, G., and Sontag, E. D. (1994). A comparison of the computational
power of sigmoid and Boolean threshold circuits. Theoretical Advances in Neural
Computation and Learning, pages 127–151. 196
Maass, W., Natschlaeger, T., and Markram, H. (2002). Real-time computing without
stable states: A new framework for neural computation based on perturbations. Neural
Computation, 14(11), 2531–2560. 399
MacKay, D. (2003). Information Theory, Inference and Learning Algorithms. Cambridge
University Press. 71
Maclaurin, D., Duvenaud, D., and Adams, R. P. (2015). Gradient-based hyperparameter
optimization through reversible learning. arXiv preprint arXiv:1502.03492 . 430
Mao, J., Xu, W., Yang, Y., Wang, J., Huang, Z., and Yuille, A. L. (2015). Deep captioning
with multimodal recurrent neural networks. In ICLR’2015 . arXiv:1410.1090. 100
Marcotte, P. and Savard, G. (1992). Novel approaches to the discrimination problem.
Zeitschrift für Operations Research (Theory), 36, 517–545. 273
Marlin, B. and de Freitas, N. (2011). Asymptotic efficiency of deterministic estimators for
discrete energy-based models: Ratio matching and pseudolikelihood. In UAI’2011 . 615,
Marlin, B., Swersky, K., Chen, B., and de Freitas, N. (2010). Inductive principles for
restricted Boltzmann machine learning. In Proceedings of The Thirteenth International
Conference on Artificial Intelligence and Statistics (AISTATS’10), volume 9, pages
509–516. 611, 616, 617
Marquardt, D. W. (1963). An algorithm for least-squares estimation of non-linear param-
eters. Journal of the Society of Industrial and Applied Mathematics,
(2), 431–441.
Marr, D. and Poggio, T. (1976). Cooperative computation of stereo disparity. Science,
194. 361
Martens, J. (2010). Deep learning via Hessian-free optimization. In L. Bottou and
M. Littman, editors, Proceedings of the Twenty-seventh International Conference on
Machine Learning (ICML-10), pages 735–742. ACM. 300
Martens, J. and Medabalimi, V. (2014). On the expressive efficiency of sum product
networks. arXiv:1411.7717 . 551
Martens, J. and Sutskever, I. (2011). Learning recurrent neural networks with Hessian-free
optimization. In Proc. ICML’2011 . ACM. 408
Mase, S. (1995). Consistency of the maximum pseudo-likelihood estimator of continuous
state space Gibbsian processes. The Annals of Applied Probability,
(3), pp. 603–612.
McClelland, J., Rumelhart, D., and Hinton, G. (1995). The appeal of parallel distributed
processing. In Computation & intelligence, pages 305–341. American Association for
Artificial Intelligence. 16
McCulloch, W. S. and Pitts, W. (1943). A logical calculus of ideas immanent in nervous
activity. Bulletin of Mathematical Biophysics, 5, 115–133. 13, 14
Mead, C. and Ismail, M. (2012). Analog VLSI implementation of neural systems, volume 80.
Springer Science & Business Media. 446
Melchior, J., Fischer, A., and Wiskott, L. (2013). How to center binary deep Boltzmann
machines. arXiv preprint arXiv:1311.1354 . 670
Memisevic, R. and Hinton, G. E. (2007). Unsupervised learning of image transformations.
In Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR’07).
Memisevic, R. and Hinton, G. E. (2010). Learning to represent spatial transformations
with factored higher-order Boltzmann machines. Neural Computation,
(6), 1473–1492.
Mesnil, G., Dauphin, Y., Glorot, X., Rifai, S., Bengio, Y., Goodfellow, I., Lavoie, E.,
Muller, X., Desjardins, G., Warde-Farley, D., Vincent, P., Courville, A., and Bergstra,
J. (2011). Unsupervised and transfer learning challenge: a deep learning approach. In
JMLR W&CP: Proc. Unsupervised and Transfer Learning, volume 7. 197, 530, 536
Mesnil, G., Rifai, S., Dauphin, Y., Bengio, Y., and Vincent, P. (2012). Surfing on the
manifold. Learning Workshop, Snowbird. 707
Miikkulainen, R. and Dyer, M. G. (1991). Natural language processing with modular
PDP networks and distributed lexicon. Cognitive Science, 15, 343–399. 472
Mikolov, T. (2012). Statistical Language Models based on Neural Networks. Ph.D. thesis,
Brno University of Technology. 410
Mikolov, T., Deoras, A., Kombrink, S., Burget, L., and Cernocky, J. (2011a). Empirical
evaluation and combination of advanced language modeling techniques. In Proc. 12th an-
nual conference of the international speech communication association (INTERSPEECH
2011). 467
Mikolov, T., Deoras, A., Povey, D., Burget, L., and Cernocky, J. (2011b). Strategies for
training large scale neural network language models. In Proc. ASRU’2011. 324, 467
Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient estimation of word rep-
resentations in vector space. In International Conference on Learning Representations:
Workshops Track. 534
Mikolov, T., Le, Q. V., and Sutskever, I. (2013b). Exploiting similarities among languages
for machine translation. Technical report, arXiv:1309.4168. 537
Minka, T. (2005). Divergence measures and message passing. Microsoft Research Cambridge
UK Tech Rep MSRTR2005173 , 72(TR-2005-173). 623
Minsky, M. L. and Papert, S. A. (1969). Perceptrons. MIT Press, Cambridge. 14
Mirza, M. and Osindero, S. (2014). Conditional generative adversarial nets. arXiv preprint
arXiv:1411.1784 . 698
Mishkin, D. and Matas, J. (2015). All you need is a good init. arXiv preprint
arXiv:1511.06422 . 301
Misra, J. and Saha, I. (2010). Artificial neural networks in hardware: A survey of two
decades of progress. Neurocomputing, 74(1), 239–255. 446
Mitchell, T. M. (1997). Machine Learning. McGraw-Hill, New York. 97
Miyato, T., Maeda, S., Koyama, M., Nakae, K., and Ishii, S. (2015). Distributional
smoothing with virtual adversarial training. In ICLR. Preprint: arXiv:1507.00677. 266
Mnih, A. and Gregor, K. (2014). Neural variational inference and learning in belief
networks. In ICML’2014 . 688, 690
Mnih, A. and Hinton, G. E. (2007). Three new graphical models for statistical language
modelling. In Z. Ghahramani, editor, Proceedings of the Twenty-fourth International
Conference on Machine Learning (ICML’07), pages 641–648. ACM. 460
Mnih, A. and Hinton, G. E. (2009). A scalable hierarchical distributed language model.
In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, Advances in Neural
Information Processing Systems 21 (NIPS’08), pages 1081–1088. 462
Mnih, A. and Kavukcuoglu, K. (2013). Learning word embeddings efficiently with noise-
contrastive estimation. In C. Burges, L. Bottou, M. Welling, Z. Ghahramani, and
K. Weinberger, editors, Advances in Neural Information Processing Systems 26 , pages
2265–2273. Curran Associates, Inc. 467, 620
Mnih, A. and Teh, Y. W. (2012). A fast and simple algorithm for training neural
probabilistic language models. In ICML’2012 , pages 1751–1758. 467
Mnih, V. and Hinton, G. (2010). Learning to detect roads in high-resolution aerial images.
In Proceedings of the 11th European Conference on Computer Vision (ECCV). 100
Mnih, V., Larochelle, H., and Hinton, G. (2011). Conditional restricted Boltzmann
machines for structure output prediction. In Proc. Conf. on Uncertainty in Artificial
Intelligence (UAI). 682
Mnih, V., Kavukcuoglo, K., Silver, D., Graves, A., Antonoglou, I., and Wierstra, D. (2013).
Playing Atari with deep reinforcement learning. Technical report, arXiv:1312.5602. 104
Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K. (2014). Recurrent models of visual
attention. In Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger,
editors, NIPS’2014 , pages 2204–2212. 688
Mnih, V., Kavukcuoglo, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves,
A., Riedmiller, M., Fidgeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A.,
Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. (2015).
Human-level control through deep reinforcement learning. Nature, 518, 529–533. 25
Mobahi, H. and Fisher, III, J. W. (2015). A theoretical analysis of optimization by
Gaussian continuation. In AAAI’2015 . 323
Mobahi, H., Collobert, R., and Weston, J. (2009). Deep learning from temporal coherence
in video. In L. Bottou and M. Littman, editors, Proceedings of the 26th International
Conference on Machine Learning, pages 737–744, Montreal. Omnipress. 490
Mohamed, A., Dahl, G., and Hinton, G. (2009). Deep belief networks for phone recognition.
Mohamed, A., Sainath, T. N., Dahl, G., Ramabhadran, B., Hinton, G. E., and Picheny,
M. A. (2011). Deep belief networks using discriminative features for phone recognition. In
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference
on, pages 5060–5063. IEEE. 454
Mohamed, A., Dahl, G., and Hinton, G. (2012a). Acoustic modeling using deep belief
networks. IEEE Trans. on Audio, Speech and Language Processing,
(1), 14–22. 454
Mohamed, A., Hinton, G., and Penn, G. (2012b). Understanding how deep belief networks
perform acoustic modelling. In Acoustics, Speech and Signal Processing (ICASSP),
2012 IEEE International Conference on, pages 4273–4276. IEEE. 454
Moller, M. F. (1993). A scaled conjugate gradient algorithm for fast supervised learning.
Neural Networks, 6, 525–533. 312
Montavon, G. and Muller, K.-R. (2012). Deep Boltzmann machines and the centering
trick. In G. Montavon, G. Orr, and K.-R. Müller, editors, Neural Networks: Tricks of
the Trade, volume 7700 of Lecture Notes in Computer Science, pages 621–637. Preprint: 670
Montúfar, G. (2014). Universal approximation depth and errors of narrow belief networks
with discrete units. Neural Computation, 26. 551
Montúfar, G. and Ay, N. (2011). Refinements of universal approximation results for
deep belief networks and restricted Boltzmann machines. Neural Computation,
1306–1319. 551
Montufar, G. F., Pascanu, R., Cho, K., and Bengio, Y. (2014). On the number of linear
regions of deep neural networks. In NIPS’2014 . 18, 196, 197
Mor-Yosef, S., Samueloff, A., Modan, B., Navot, D., and Schenker, J. G. (1990). Ranking
the risk factors for cesarean: logistic regression analysis of a nationwide study. Obstet
Gynecol, 75(6), 944–7. 3
Morin, F. and Bengio, Y. (2005). Hierarchical probabilistic neural network language
model. In AISTATS’2005 . 462, 464
Mozer, M. C. (1992). The induction of multiscale temporal structure. In J. M. S. Hanson
and R. Lippmann, editors, Advances in Neural Information Processing Systems 4
(NIPS’91), pages 275–282, San Mateo, CA. Morgan Kaufmann. 403
Murphy, K. P. (2012). Machine Learning: a Probabilistic Perspective. MIT Press,
Cambridge, MA, USA. 60, 96, 142
Murray, B. U. I. and Larochelle, H. (2014). A deep and tractable density estimator. In
ICML’2014 . 186, 706, 707
Nair, V. and Hinton, G. (2010). Rectified linear units improve restricted Boltzmann
machines. In ICML’2010 . 15, 171, 193
Nair, V. and Hinton, G. E. (2009). 3d object recognition with deep belief nets. In Y. Bengio,
D. Schuurmans, J. D. Lafferty, C. K. I. Williams, and A. Culotta, editors, Advances in
Neural Information Processing Systems 22 , pages 1339–1347. Curran Associates, Inc.
Narayanan, H. and Mitter, S. (2010). Sample complexity of testing the manifold hypothesis.
In NIPS’2010 . 160
Naumann, U. (2008). Optimal Jacobian accumulation is NP-complete. Mathematical
Programming, 112(2), 427–441. 218
Navigli, R. and Velardi, P. (2005). Structural semantic interconnections: a knowledge-
based approach to word sense disambiguation. IEEE Trans. Pattern Analysis and
Machine Intelligence, 27(7), 1075––1086. 480
Neal, R. and Hinton, G. (1999). A view of the EM algorithm that justifies incremental,
sparse, and other variants. In M. I. Jordan, editor, Learning in Graphical Models. MIT
Press, Cambridge, MA. 632
Neal, R. M. (1990). Learning stochastic feedforward networks. Technical report. 689
Neal, R. M. (1993). Probabilistic inference using Markov chain Monte-Carlo methods.
Technical Report CRG-TR-93-1, Dept. of Computer Science, University of Toronto. 676
Neal, R. M. (1994). Sampling from multimodal distributions using tempered transitions.
Technical Report 9421, Dept. of Statistics, University of Toronto. 601
Neal, R. M. (1996). Bayesian Learning for Neural Networks. Lecture Notes in Statistics.
Springer. 262
Neal, R. M. (2001). Annealed importance sampling. Statistics and Computing,
125–139. 623, 625, 626, 627
Neal, R. M. (2005). Estimating ratios of normalizing constants using linked importance
sampling. 627
Nesterov, Y. (1983). A method of solving a convex programming problem with convergence
rate O(1/k
). Soviet Mathematics Doklady, 27, 372–376. 296
Nesterov, Y. (2004). Introductory lectures on convex optimization : a basic course. Applied
optimization. Kluwer Academic Publ., Boston, Dordrecht, London. 296
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y. (2011). Reading
digits in natural images with unsupervised feature learning. Deep Learning and
Unsupervised Feature Learning Workshop, NIPS. 19
Ney, H. and Kneser, R. (1993). Improved clustering techniques for class-based statistical
language modelling. In European Conference on Speech Communication and Technology
(Eurospeech), pages 973–976, Berlin. 458
Ng, A. (2015). Advice for applying machine learning. 416
Niesler, T. R., Whittaker, E. W. D., and Woodland, P. C. (1998). Comparison of part-of-
speech and automatically derived category-based language models for speech recognition.
In International Conference on Acoustics, Speech and Signal Processing (ICASSP),
pages 177–180. 458
Ning, F., Delhomme, D., LeCun, Y., Piano, F., Bottou, L., and Barbano, P. E. (2005).
Toward automatic phenotyping of developing embryos from videos. Image Processing,
IEEE Transactions on, 14(9), 1360–1371. 353
Nocedal, J. and Wright, S. (2006). Numerical Optimization. Springer. 90, 93
Norouzi, M. and Fleet, D. J. (2011). Minimal loss hashing for compact binary codes. In
ICML’2011 . 523
Nowlan, S. J. (1990). Competing experts: An experimental investigation of associative
mixture models. Technical Report CRG-TR-90-5, University of Toronto. 445
Nowlan, S. J. and Hinton, G. E. (1992). Simplifying neural networks by soft weight-sharing.
Neural Computation, 4(4), 473–493. 137
Olshausen, B. and Field, D. J. (2005). How close are we to understanding V1? Neural
Computation, 17, 1665–1699. 15
Olshausen, B. A. and Field, D. J. (1996). Emergence of simple-cell receptive field properties
by learning a sparse code for natural images. Nature,
, 607–609. 144, 252, 364, 492
Olshausen, B. A., Anderson, C. H., and Van Essen, D. C. (1993). A neurobiological
model of visual attention and invariant pattern recognition based on dynamic routing
of information. J. Neurosci., 13(11), 4700–4719. 445
Opper, M. and Archambeau, C. (2009). The variational Gaussian approximation revisited.
Neural computation, 21(3), 786–792. 685
Oquab, M., Bottou, L., Laptev, I., and Sivic, J. (2014). Learning and transferring mid-level
image representations using convolutional neural networks. In Computer Vision and
Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 1717–1724. IEEE. 534
Osindero, S. and Hinton, G. E. (2008). Modeling image patches with a directed hierarchy
of Markov random fields. In J. Platt, D. Koller, Y. Singer, and S. Roweis, editors,
Advances in Neural Information Processing Systems 20 (NIPS’07), pages 1121–1128,
Cambridge, MA. MIT Press. 630
Ovid and Martin, C. (2004). Metamorphoses. W.W. Norton. 1
Paccanaro, A. and Hinton, G. E. (2000). Extracting distributed representations of concepts
and relations from positive and negative propositions. In International Joint Conference
on Neural Networks (IJCNN), Como, Italy. IEEE, New York. 479
Paine, T. L., Khorrami, P., Han, W., and Huang, T. S. (2014). An analysis of unsupervised
pre-training in light of recent advances. arXiv preprint arXiv:1412.6597 . 530
Palatucci, M., Pomerleau, D., Hinton, G. E., and Mitchell, T. M. (2009). Zero-shot
learning with semantic output codes. In Y. Bengio, D. Schuurmans, J. D. Lafferty,
C. K. I. Williams, and A. Culotta, editors, Advances in Neural Information Processing
Systems 22 , pages 1410–1418. Curran Associates, Inc. 537
Parker, D. B. (1985). Learning-logic. Technical Report TR-47, Center for Comp. Research
in Economics and Management Sci., MIT. 221
Pascanu, R., Mikolov, T., and Bengio, Y. (2013). On the difficulty of training recurrent
neural networks. In ICML’2013 . 285, 396, 399, 403, 409, 410, 411
Pascanu, R., Gülçehre, Ç., Cho, K., and Bengio, Y. (2014a). How to construct deep
recurrent neural networks. In ICLR’2014 . 18, 262, 393, 394, 406, 455
Pascanu, R., Montufar, G., and Bengio, Y. (2014b). On the number of inference regions
of deep feed forward networks with piece-wise linear activations. In ICLR’2014 . 548
Pati, Y., Rezaiifar, R., and Krishnaprasad, P. (1993). Orthogonal matching pursuit:
Recursive function approximation with applications to wavelet decomposition. In Pro-
ceedings of the 27 th Annual Asilomar Conference on Signals, Systems, and Computers,
pages 40–44. 252
Pearl, J. (1985). Bayesian networks: A model of self-activated memory for evidential
reasoning. In Proceedings of the 7th Conference of the Cognitive Science Society,
University of California, Irvine, pages 329–334. 560
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible
Inference. Morgan Kaufmann. 52
Perron, O. (1907). Zur theorie der matrices. Mathematische Annalen,
(2), 248–263. 594
Petersen, K. B. and Pedersen, M. S. (2006). The matrix cookbook. Version 20051003. 29
Peterson, G. B. (2004). A day of great illumination: B.