Chapter 18
Confronting the Partition
Function
In section 16.2.2 we saw that many probabilistic models (commonly known as undi-
rected graphical models) are deﬁned by an unnormalized probability distribution
˜p
(
x
;
θ
). We must normalize
˜p
by dividing by a partition function
Z
(
θ
) to obtain a
valid probability distribution:
p(x; θ) =
1
Z(θ)
˜p(x; θ). (18.1)
The partition function is an integral (for continuous variables) or sum (for discrete
variables) over the unnormalized probability of all states:
˜p(x)dx (18.2)
or
x
˜p(x). (18.3)
This operation is intractable for many interesting models.
As we will see in chapter 20, several deep learning models are designed to have
a tractable normalizing constant, or are designed to be used in ways that do not
involve computing
p
(
x
) at all. Yet, other models directly confront the challenge of
intractable partition functions. In this chapter, we describe techniques used for
training and evaluating models that have intractable partition functions.
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CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
What makes learning undirected models by maximum likelihood particularly
diﬃcult is that the partition function depends on the parameters. The gradient of
the log-likelihood with respect to the parameters has a term corresponding to the
θ
log p(x; θ) =
θ
log ˜p(x; θ)
θ
log Z(θ). (18.4)
This is a well-known decomposition into the
positive phase
and
negative
phase of learning.
For most undirected models of interest, the negative phase is diﬃcult. Models
with no latent variables or with few interactions between latent variables typically
have a tractable positive phase. The quintessential example of a model with a
straightforward positive phase and a diﬃcult negative phase is the RBM, which has
hidden units that are conditionally independent from each other given the visible
units. The case where the positive phase is diﬃcult, with complicated interactions
between latent variables, is primarily covered in chapter 19. This chapter focuses
on the diﬃculties of the negative phase.
Let us look more closely at the gradient of log Z:
θ
log Z (18.5)
=
θ
Z
Z
(18.6)
=
θ
x
˜p(x)
Z
(18.7)
=
x
θ
˜p(x)
Z
. (18.8)
For models that guarantee
p
(
x
)
>
0 for all
x
, we can substitute
exp (log ˜p(x))
for ˜p(x):
x
θ
exp (log ˜p(x))
Z
(18.9)
=
x
exp (log ˜p(x))
θ
log ˜p(x)
Z
(18.10)
=
x
˜p(x)
θ
log ˜p(x)
Z
(18.11)
=
x
p(x)
θ
log ˜p(x) (18.12)
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CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
= E
xp(x)
θ
log ˜p(x). (18.13)
This derivation made use of summation over discrete
x
, but a similar result
applies using integration over continuous
x
. In the continuous version of the
derivation, we use Leibniz’s rule for diﬀerentiation under the integral sign to obtain
the identity
θ
˜p(x)dx =
θ
˜p(x)dx. (18.14)
This identity is applicable only under certain regularity conditions on
˜p
and
θ
˜p
(
x
).
In measure theoretic terms, the conditions are: (1) The unnormalized distribution
˜p
must be a Lebesgue-integrable function of
x
for every value of
θ
θ
˜p
(
x
) must exist for all
θ
and almost all
x
. (3) There must exist an integrable
function
R
(
x
) that bounds
θ
˜p
(
x
) in the sense that
max
i
|
θ
i
˜p
(
x
)
| R
(
x
) for all
θ
and almost all
x
. Fortunately, most machine learning models of interest have
these properties.
This identity
θ
log Z = E
xp(x)
θ
log ˜p(x) (18.15)
is the basis for a variety of Monte Carlo methods for approximately maximizing
the likelihood of models with intractable partition functions.
The Monte Carlo approach to learning undirected models provides an intuitive
framework in which we can think of both the positive phase and the negative
phase. In the positive phase, we increase
log ˜p
(
x
) for
x
drawn from the data. In
the negative phase, we decrease the partition function by decreasing
log ˜p
(
x
) drawn
from the model distribution.
In the deep learning literature, it is common to parametrize
log ˜p
in terms of
an energy function (equation 16.7). In this case, we can interpret the positive
phase as pushing down on the energy of training examples and the negative phase
as pushing up on the energy of samples drawn from the model, as illustrated in
ﬁgure 18.1.
18.2 Stochastic Maximum Likelihood and Contrastive
Divergence
The naive way of implementing equation 18.15 is to compute it by burning in
a set of Markov chains from a random initialization every time the gradient is
needed. When learning is performed using stochastic gradient descent, this means
the chains must be burned in once per gradient step. This approach leads to the
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CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
Algorithm 18.1
A naive MCMC algorithm for maximizing the log-likelihood
with an intractable partition function using gradient ascent
Set , the step size, to a small positive number.
Set
k
, the number of Gibbs steps, high enough to allow burn in. Perhaps 100 to
train an RBM on a small image patch.
while not converged do
Sample a minibatch of m examples {x
(1)
, . . . , x
(m)
} from the training set
g
1
m
m
i=1
θ
log ˜p(x
(i)
; θ).
Initialize a set of
m
samples
{
˜
x
(1)
, . . . ,
˜
x
(m)
}
to random values (e.g., from
a uniform or normal distribution, or possibly a distribution with marginals
matched to the model’s marginals).
for i = 1 to k do
for j = 1 to m do
˜
x
(j)
gibbs_update(
˜
x
(j)
).
end for
end for
g g
1
m
m
i=1
θ
log ˜p(
˜
x
(i)
; θ).
θ θ + g.
end while
training procedure presented in algorithm 18.1. The high cost of burning in the
Markov chains in the inner loop makes this procedure computationally infeasible,
but this procedure is the starting point that other more practical algorithms aim
to approximate.
We can view the MCMC approach to maximum likelihood as trying to achieve
balance between two forces, one pushing up on the model distribution where the
data occurs, and another pushing down on the model distribution where the model
samples occur. Figure 18.1 illustrates this process. The two forces correspond to
maximizing
log ˜p
and minimizing
log Z
. Several approximations to the negative
phase are possible. Each of these approximations can be understood as making
the negative phase computationally cheaper but also making it push down in the
wrong locations.
Because the negative phase involves drawing samples from the model’s distri-
bution, we can think of it as ﬁnding points that the model believes in strongly.
Because the negative phase acts to reduce the probability of those points, they
are generally considered to represent the model’s incorrect beliefs about the world.
They are frequently referred to in the literature as “hallucinations” or “fantasy
particles.” In fact, the negative phase has been proposed as a possible explanation
606
CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
x
p(x)
The positive phase
p
model
(x)
p
data
(x)
x
p(x)
The negative phase
p
model
(x)
p
data
(x)
Figure 18.1: The view of algorithm 18.1 as having a “positive phase” and a “negative
phase.” (Left)In the positive phase, we sample points from the data distribution and
push up on their unnormalized probability. This means points that are likely in the
data get pushed up on more. (Right)In the negative phase, we sample points from the
model distribution and push down on their unnormalized probability. This counteracts
the positive phase’s tendency to just add a large constant to the unnormalized probability
everywhere. When the data distribution and the model distribution are equal, the positive
phase has the same chance to push up at a point as the negative phase has to push down.
When this occurs, there is no longer any gradient (in expectation), and training must
terminate.
for dreaming in humans and other animals (Crick and Mitchison, 1983), the idea
being that the brain maintains a probabilistic model of the world and follows the
log ˜p
when experiencing real events while awake and follows the negative
log ˜p
to minimize
log Z
while sleeping and experiencing events sampled
from the current model. This view explains much of the language used to describe
algorithms with a positive and a negative phase, but it has not been proved to be
correct with neuroscientiﬁc experiments. In machine learning models, it is usually
necessary to use the positive and negative phase simultaneously, rather than in
separate periods of wakefulness and REM sleep. As we will see in section 19.5,
other machine learning algorithms draw samples from the model distribution for
other purposes, and such algorithms could also provide an account for the function
of dream sleep.
Given this understanding of the role of the positive and the negative phase of
learning, we can attempt to design a less expensive alternative to algorithm 18.1.
The main cost of the naive MCMC algorithm is the cost of burning in the Markov
chains from a random initialization at each step. A natural solution is to initialize
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CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
Algorithm 18.2
The contrastive divergence algorithm, using gradient ascent as
the optimization procedure
Set , the step size, to a small positive number.
Set
k
, the number of Gibbs steps, high enough to allow a Markov chain sampling
from
p
(
x
;
θ
) to mix when initialized from
p
data
. Perhaps 1–20 to train an RBM
on a small image patch.
while not converged do
Sample a minibatch of m examples {x
(1)
, . . . , x
(m)
} from the training set
g
1
m
m
i=1
θ
log ˜p(x
(i)
; θ).
for i = 1 to m do
˜
x
(i)
x
(i)
.
end for
for i = 1 to k do
for j = 1 to m do
˜
x
(j)
gibbs_update(
˜
x
(j)
).
end for
end for
g g
1
m
m
i=1
θ
log ˜p(
˜
x
(i)
; θ).
θ θ + g.
end while
the Markov chains from a distribution that is very close to the model distribution,
so that the burn in operation does not take as many steps.
The
contrastive divergence
(CD, or CD-
k
to indicate CD with
k
Gibbs steps)
algorithm initializes the Markov chain at each step with samples from the data
distribution (Hinton, 2000, 2010). This approach is presented as algorithm 18.2.
Obtaining samples from the data distribution is free, because they are already
available in the dataset. Initially, the data distribution is not close to the model
distribution, so the negative phase is not very accurate. Fortunately, the positive
phase can still accurately increase the model’s probability of the data. After the
positive phase has had some time to act, the model distribution is closer to the
data distribution, and the negative phase starts to become accurate.
Of course, CD is still an approximation to the correct negative phase. The
main way in which CD qualitatively fails to implement the correct negative phase
is that it fails to suppress regions of high probability that are far from actual
training examples. These regions that have high probability under the model
but low probability under the data-generating distribution are called
spurious
modes
. Figure 18.2 illustrates why this happens. Essentially, modes in the model
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CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
x
p(x)
p
model
(x)
p
data
(x)
Figure 18.2: A spurious mode. An illustration of how the negative phase of contrastive
divergence (algorithm 18.2) can fail to suppress spurious modes. A spurious mode is
a mode that is present in the model distribution but absent in the data distribution.
Because contrastive divergence initializes its Markov chains from data points and runs the
Markov chain for only a few steps, it is unlikely to visit modes in the model that are far
from the data points. This means that when sampling from the model, we will sometimes
get samples that do not resemble the data. It also means that due to wasting some of
its probability mass on these modes, the model will struggle to place high probability
mass on the correct modes. For the purpose of visualization, this ﬁgure uses a somewhat
simpliﬁed concept of distance—the spurious mode is far from the correct mode along the
number line in
R
. This corresponds to a Markov chain based on making local moves with
a single
x
variable in
R
. For most deep probabilistic models, the Markov chains are based
on Gibbs sampling and can make nonlocal moves of individual variables but cannot move
all the variables simultaneously. For these problems, it is usually better to consider the
edit distance between modes, rather than the Euclidean distance. However, edit distance
in a high-dimensional space is diﬃcult to depict in a 2-D plot.
distribution that are far from the data distribution will not be visited by Markov
chains initialized at training points, unless k is very large.
Carreira-Perpiñan and Hinton (2005) showed experimentally that the CD
estimator is biased for RBMs and fully visible Boltzmann machines, in that it
converges to diﬀerent points than the maximum likelihood estimator. They argue
that because the bias is small, CD could be used as an inexpensive way to initialize
a model that could later be ﬁne-tuned via more expensive MCMC methods. Bengio
and Delalleau (2009) show that CD can be interpreted as discarding the smallest
terms of the correct MCMC update gradient, which explains the bias.
CD is useful for training shallow models like RBMs. These can in turn be
stacked to initialize deeper models like DBNs or DBMs. But CD does not provide
609
CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
much help for training deeper models directly. This is because it is diﬃcult to
obtain samples of the hidden units given samples of the visible units. Since the
hidden units are not included in the data, initializing from training points cannot
solve the problem. Even if we initialize the visible units from the data, we will still
need to burn in a Markov chain sampling from the distribution over the hidden
units conditioned on those visible samples.
The CD algorithm can be thought of as penalizing the model for having a
Markov chain that changes the input rapidly when the input comes from the data.
This means training with CD somewhat resembles autoencoder training. Even
though CD is more biased than some of the other training methods, it can be
useful for pretraining shallow models that will later be stacked. This is because
the earliest models in the stack are encouraged to copy more information up to
their latent variables, thereby making it available to the later models. This should
be thought of more as an often-exploitable side eﬀect of CD training rather than a
Sutskever and Tieleman (2010) showed that the CD update direction is not the
gradient of any function. This allows for situations where CD could cycle forever,
but in practice this is not a serious problem.
A diﬀerent strategy that resolves many of the problems with CD is to initial-
ize the Markov chains at each gradient step with their states from the previous
gradient step. This approach was ﬁrst discovered under the name
stochastic max-
imum likelihood
(SML) in the applied mathematics and statistics community
(Younes, 1998) and later independently rediscovered under the name
persistent
contrastive divergence
(PCD, or PCD-
k
to indicate the use of
k
Gibbs steps
per update) in the deep learning community (Tieleman, 2008). See algorithm 18.3.
The basic idea of this approach is that, as long as the steps taken by the stochastic
gradient algorithm are small, the model from the previous step will be similar to
the model from the current step. It follows that the samples from the previous
model’s distribution will be very close to being fair samples from the current
model’s distribution, so a Markov chain initialized with these samples will not
require much time to mix.
Because each Markov chain is continually updated throughout the learning
process, rather than restarted at each gradient step, the chains are free to wander
far enough to ﬁnd all the model’s modes. SML is thus considerably more resistant
to forming models with spurious modes than CD is. Moreover, because it is possible
to store the state of all the sampled variables, whether visible or latent, SML
provides an initialization point for both the hidden and the visible units. CD is
only able to provide an initialization for the visible units, and therefore requires
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CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
Algorithm 18.3
The stochastic maximum likelihood / persistent contrastive
divergence algorithm using gradient ascent as the optimization procedure
Set , the step size, to a small positive number.
Set
k
, the number of Gibbs steps, high enough to allow a Markov chain sampling
from
p
(
x
;
θ
+
g
) to burn in, starting from samples from
p
(
x
;
θ
). Perhaps 1 for
RBM on a small image patch, or 5–50 for a more complicated model like a DBM.
Initialize a set of
m
samples
{
˜
x
(1)
, . . . ,
˜
x
(m)
}
to random values (e.g., from a
uniform or normal distribution, or possibly a distribution with marginals matched
to the model’s marginals).
while not converged do
Sample a minibatch of m examples {x
(1)
, . . . , x
(m)
} from the training set
g
1
m
m
i=1
θ
log ˜p(x
(i)
; θ).
for i = 1 to k do
for j = 1 to m do
˜
x
(j)
gibbs_update(
˜
x
(j)
).
end for
end for
g g
1
m
m
i=1
θ
log ˜p(
˜
x
(i)
; θ).
θ θ + g.
end while
burn-in for deep models. SML is able to train deep models eﬃciently. Marlin
et al. (2010) compared SML to many other criteria presented in this chapter. They
found that SML results in the best test set log-likelihood for an RBM, and that if
the RBM’s hidden units are used as features for an SVM classiﬁer, SML results in
the best classiﬁcation accuracy.
SML is vulnerable to becoming inaccurate if the stochastic gradient algorithm
can move the model faster than the Markov chain can mix between steps. This
can happen if
k
is too small or
is too large. The permissible range of values is
unfortunately highly problem dependent. There is no known way to test formally
whether the chain is successfully mixing between steps. Subjectively, if the learning
rate is too high for the number of Gibbs steps, the human operator will be able to
observe much more variance in the negative phase samples across gradient steps
than across diﬀerent Markov chains. For example, a model trained on MNIST
might sample exclusively 7s on one step. The learning process will then push down
strongly on the mode corresponding to 7s, and the model might sample exclusively
9s on the next step.
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CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
Care must be taken when evaluating the samples from a model trained with
SML. It is necessary to draw the samples starting from a fresh Markov chain
initialized from a random starting point after the model is done training. The
samples present in the persistent negative chains used for training have been
inﬂuenced by several recent versions of the model, and thus can make the model
appear to have greater capacity than it actually does.
Berglund and Raiko (2013) performed experiments to examine the bias and
variance in the estimate of the gradient provided by CD and SML. CD proves to
have lower variance than the estimator based on exact sampling. SML has higher
variance. The cause of CD’s low variance is its use of the same training points
in both the positive and negative phase. If the negative phase is initialized from
diﬀerent training points, the variance rises above that of the estimator based on
exact sampling.
All these methods based on using MCMC to draw samples from the model can
in principle be used with almost any variant of MCMC. This means that techniques
such as SML can be improved by using any of the enhanced MCMC techniques
described in chapter 17, such as parallel tempering (Desjardins et al., 2010; Cho
et al., 2010).
One approach to accelerating mixing during learning relies not on changing
the Monte Carlo sampling technology but rather on changing the parametrization
of the model and the cost function.
Fast PCD
, or FPCD (Tieleman and Hinton,
2009) involves replacing the parameters
θ
of a traditional model with an expression
θ = θ
(slow)
+ θ
(fast)
. (18.16)
There are now twice as many parameters as before, and they are added together
element-wise to provide the parameters used by the original model deﬁnition. The
fast copy of the parameters is trained with a much larger learning rate, allowing
it to adapt rapidly in response to the negative phase of learning and push the
Markov chain to new territory. This forces the Markov chain to mix rapidly, though
this eﬀect occurs only during learning while the fast weights are free to change.
Typically one also applies signiﬁcant weight decay to the fast weights, encouraging
them to converge to small values, after only transiently taking on large values long
enough to encourage the Markov chain to change modes.
One key beneﬁt to the MCMC-based methods described in this section is that
they provide an estimate of the gradient of
log Z
, and thus we can essentially
decompose the problem into the
log ˜p
contribution and the
log Z
contribution.
We can then use any other method to tackle
log ˜p
(
x
) and just add our negative
phase gradient onto the other method’s gradient. In particular, this means that
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CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
our positive phase can make use of methods that provide only a lower bound on
˜p
. Most of the other methods of dealing with
log Z
presented in this chapter are
incompatible with bound-based positive phase methods.
18.3 Pseudolikelihood
Monte Carlo approximations to the partition function and its gradient directly
confront the partition function. Other approaches sidestep the issue, by training
the model without computing the partition function. Most of these approaches are
based on the observation that it is easy to compute ratios of probabilities in an
undirected probabilistic model. This is because the partition function appears in
both the numerator and the denominator of the ratio and cancels out:
p(x)
p(y)
=
1
Z
˜p(x)
1
Z
˜p(y)
=
˜p(x)
˜p(y)
. (18.17)
The pseudolikelihood is based on the observation that conditional probabilities
take this ratio-based form and thus can be computed without knowledge of the
partition function. Suppose that we partition
x
into
a
,
b
and
c
, where
a
contains
the variables we want to ﬁnd the conditional distribution over,
b
contains the
variables we want to condition on, and
c
contains the variables that are not part
of our query:
p(a | b) =
p(a, b)
p(b)
=
p(a, b)
a,c
p(a, b, c)
=
˜p(a, b)
a,c
˜p(a, b, c)
. (18.18)
This quantity requires marginalizing out
a
, which can be a very eﬃcient operation
provided that
a
and
c
do not contain many variables. In the extreme case,
a
can
be a single variable and
c
can be empty, making this operation require only as
many evaluations of ˜p as there are values of a single random variable.
Unfortunately, in order to compute the log-likelihood, we need to marginalize
out large sets of variables. If there are
n
variables total, we must marginalize a set
of size n 1. By the chain rule of probability,
log p(x) = log p(x
1
) + log p(x
2
| x
1
) + ··· + p(x
n
| x
1:n1
). (18.19)
In this case, we have made
a
maximally small, but
c
can be as large as
x
2:n
. What
if we simply move
c
into
b
to reduce the computational cost? This yields the
pseudolikelihood
(Besag, 1975) objective function, based on predicting the value
613
CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
of feature x
i
given all the other features x
i
:
n
i=1
log p(x
i
| x
i
). (18.20)
If each random variable has
k
diﬀerent values, this requires only
k×n
evaluations
of
˜p
to compute, as opposed to the
k
n
evaluations needed to compute the partition
function.
This may look like an unprincipled hack, but it can be proved that estimation
by maximizing the pseudolikelihood is asymptotically consistent (Mase, 1995).
Of course, in the case of datasets that do not approach the large sample limit,
pseudolikelihood may display diﬀerent behavior from the maximum likelihood
estimator.
It is possible to trade computational complexity for deviation from maxi-
mum likelihood behavior by using the
generalized pseudolikelihood estima-
tor
(Huang and Ogata, 2002). The generalized pseudolikelihood estimator uses
m
diﬀerent sets
S
(i)
, i
= 1
, . . . , m
of indices of variables that appear together
on the left side of the conditioning bar. In the extreme case of
m
= 1 and
S
(1)
=
1, . . . , n
, the generalized pseudolikelihood recovers the log-likelihood. In the
extreme case of
m
=
n
and
S
(i)
=
{i}
, the generalized pseudolikelihood recovers
the pseudolikelihood. The generalized pseudolikelihood objective function is given
by
m
i=1
log p(x
S
(i)
| x
S
(i)
). (18.21)
The performance of pseudolikelihood-based approaches depends largely on how
the model will be used. Pseudolikelihood tends to perform poorly on tasks that
require a good model of the full joint
p
(
x
), such as density estimation and sampling.
It can perform better than maximum likelihood for tasks that require only the
conditional distributions used during training, such as ﬁlling in small amounts of
missing values. Generalized pseudolikelihood techniques are especially powerful if
the data has regular structure that allows the
S
index sets to be designed to capture
the most important correlations while leaving out groups of variables that have
only negligible correlation. For example, in natural images, pixels that are widely
separated in space also have weak correlation, so the generalized pseudolikelihood
can be applied with each S set being a small, spatially localized window.
One weakness of the pseudolikelihood estimator is that it cannot be used
with other approximations that provide only a lower bound on
˜p
(
x
), such as
variational inference, which is covered in chapter 19. This is because
˜p
appears
614
CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
in the denominator. A lower bound on the denominator provides only an upper
bound on the expression as a whole, and there is no beneﬁt to maximizing an
upper bound. This makes it diﬃcult to apply pseudolikelihood approaches to deep
models such as deep Boltzmann machines, since variational methods are one of
the dominant approaches to approximately marginalizing out the many layers of
hidden variables that interact with each other. Nonetheless, pseudolikelihood is
still useful for deep learning, because it can be used to train single-layer models
or deep models using approximate inference methods that are not based on lower
bounds.
Pseudolikelihood has a much greater cost per gradient step than SML, due to
its explicit computation of all the conditionals. But generalized pseudolikelihood
and similar criteria can still perform well if only one randomly selected condi-
tional is computed per example (Goodfellow et al., 2013b), thereby bringing the
computational cost down to match that of SML.
Though the pseudolikelihood estimator does not explicitly minimize
log Z
, it
can still be thought of as having something resembling a negative phase. The
denominators of each conditional distribution result in the learning algorithm
suppressing the probability of all states that have only one variable diﬀering from
a training example.
See Marlin and de Freitas (2011) for a theoretical analysis of the asymptotic
eﬃciency of pseudolikelihood.
18.4 Score Matching and Ratio Matching
Score matching (Hyvärinen, 2005) provides another consistent means of training a
model without estimating
Z
or its derivatives. The name score matching comes
from terminology in which the derivatives of a log density with respect to its
argument,
x
log p
(
x
), are called its
score
. The strategy used by score matching
is to minimize the expected squared diﬀerence between the derivatives of the
model’s log density with respect to the input and the derivatives of the data’s log
density with respect to the input:
L(x, θ) =
1
2
||∇
x
log p
model
(x; θ), −∇
x
log p
data
(x)||
2
2
, (18.22)
J(θ) =
1
2
E
p
data
(x)
L(x, θ), (18.23)
θ
= min
θ
J(θ). (18.24)
This objective function avoids the diﬃculties associated with diﬀerentiating
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CHAPTER 18. CONFRONTING THE PARTITION FUNCTION
the partition function
Z
because
Z
is not a function of
x
and therefore
x
Z
= 0.
Initially, score matching appears to have a new diﬃculty: computing the score
of the data distribution requires knowledge of the true distribution generating
the training data, p
data
. Fortunately, minimizing the expected value of L(x, θ) is
equivalent to minimizing the expected value of
˜
L(x, θ) =
n
j=1
2
x
2
j
log p
model
(x; θ) +
1
2
x
j
log p
model
(x; θ)
2
, (18.25)
where n is the dimensionality of x.
Because score matching requires taking derivatives with respect to
x
, it is not
applicable to models of discrete data but the latent variables in the model may be
discrete.
Like pseudolikelihood, score matching only works when we are able to evaluate
log ˜p
(
x
) and its derivatives directly. It is not compatible with methods that provide
only a lower bound on
log ˜p
(
x
), because score matching requires the derivatives
and second derivatives of
log ˜p
(
x
), and a lower bound conveys no information about
its derivatives. This means that score matching cannot be applied to estimating
models with complicated interactions between the hidden units, such as sparse
coding models or deep Boltzmann machines. While score matching can be used
to pretrain the ﬁrst hidden layer of a larger model, it has not been applied as
a pretraining strategy for the deeper layers of a larger model. This is probably
because the hidden layers of such models usually contain some discrete variables.
While score matching does not explicitly have a negative phase, it can be
viewed as a version of contrastive div