Chapter 17
Representation Learning
What is a good representation? Many answers are possible, and this remains a
question to be further explored in future research. What we propose as answer
in this book is that in general, a good representation is one that makes further
learning tasks easy. In an unsupervised learning setting, this could mean that the
joint distribution of the different elements of the representation (e.g., elements of
the representation vector h) is one that is easy to model (e.g., in the extreme,
these elements are marginally independent of each other). But that would not
be enough: a representation that throws away all information (e.g., h = 0 for
all inputs x) is very easy to model but is also useless. Hence we want to learn a
representation that keeps the information (or at least all the relevant information,
in the supervised case) and makes it easy to learn functions of interest from this
representation.
In Chapter 1, we have introduced the notion of representation, the idea that
some representations were more helpful (e.g. to classify objects from images or
phonemes from speech) than others. As argued there, this suggests learning repre-
sentations in order to “select” the best ones in a systematic way, i.e., by optimizing
a function that maps raw data to its representation, instead of - or in addition
to - handcrafting them. This motivation for learning input features is discussed
in Section 6.5, and is one of the major side-effects of training a feedforward deep
network (treated in Chapter 6), typically via supervised learning, i.e., when one
has access to (input,target) pairs
1
, available for some task of interest. In the case
of supervised learning of deep nets, we learn a representation with the objective
of selecting one that is best suited to the task of predicting targets given inputs.
Whereas supervised learning has been the workhorse of recent industrial suc-
cesses of deep learning, the authors of this book believe that it is likely that a key
1
typically obtained by labeling inputs with some target answer that we wish the computer
would produce
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element of future advances will be unsupervised learning of representations.
So how can we exploit the information in data if we don’t have labeled exam-
ples? Or too few? Pure supervised learning with few labeled examples can easily
overfit. On the other hand, humans (and other animals) can sometimes learn a
task from just one or very few examples. How is that possible? Clearly they must
rely on previously acquired knowledge, either innate or (more likely the case for
humans) via previous learning experience. Can we discover good representations
purely out of unlabeled examples? (this is treated in the first four sections of this
chapter). Can we combine unlabeled examples (which are often easy to obtain)
with labeled examples? (this is semi-supervised learning, Section 17.3). And what
if instead of one task we have many tasks that could share the same representa-
tion or parts of it? (this is multi-task learning, discussed in Section 7.12). What
if we have “training tasks” (on which enough labeled examples are available) as
well as “test tasks” (not known at the time of learning the representation, and for
which only very few labeled examples will be provided)? What if the test task is
similar but different from the training task? (this is transfer learning and domain
adaptation, discussed in Section 17.2).
17.1 Greedy Layerwise Unsupervised Pre-Training
Unsupervised learning played a key historical role in the revival of deep neural
networks, allowing for the first time to train a deep supervised network. We
call this procedure unsupervised pre-training, or more precisely, greedy layer-wise
unsupervised pre-training, and it is the topic of this section.
This recipe relies on a one-layer representation learning algorithm such as
those introduced in this part of the book, i.e., the auto-encoders (Chapter 16)
and the RBM (Section 21.2). Each layer is pre-trained by unsupervised learning,
taking the output of the previous layer and producing as output a new represen-
tation of the data, whose distribution (or its relation to other variables such as
categories to predict) is hopefully simpler.
Greedy layerwise unsupervised pre-training was introduced in Hinton et al.
(2006); Hinton and Salakhutdinov (2006); Bengio et al. (2007); Ranzato et al.
(2007). These papers are generally credited with founding the renewed interest in
learning deep models as it provided a means of initializing subsequent supervised
training and often led to notable performance gains when compared to models
trained without unsupervised pretraining, at least for the small kinds of datasets
(like the 60,000 examples of MNIST) used in these experiments.
It is called layerwise because it proceeds one layer at a time, training the
k-th layer while keeping the previous ones fixed. It is called unsupervised because
each layer is trained with an unsupervised representation learning algorithm. It
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is called greedy because the different layers are not jointly trained with respect
to a global training objective, which could make the procedure sub-optimal. In
particular, the lower layers (which are first trained) are not adapted after the
upper layers are introduced. However it is also called pre-training, because it is
supposed to be only a first step before a joint training algorithm is applied to fine-
tune all the layers together with respect to a criterion of interest. In the context
of a supervised learning task, it can be viewed as a regularizer (see Chapter 7)
and a sophisticated form of parameter initialization.
When we refer to pre-training we will be referring to a specific protocol with
two main phases of training: the pretraining phase and the fine-tuning phase.
No matter what kind of unsupervised learning algorithm or what model type you
employ, in the vast majority of cases, the overall training scheme is nearly the
same. While the choice of unsupervised learning algorithm will obviously impact
the details, in the abstract, most applications of unsupervised pre-training follows
this basic protocol.
As outlined in Algorithm 17.1, in the pretraining phase, the layers of the
model are trained, in order, in an unsupervised way on their input, beginning
with the bottom layer, i.e. the one in direct contact with the input data. Next,
the second lowest layer is trained taking the activations of the first layer hidden
units as input for unsupervised training. Pretraining proceeds in this fashion,
from bottom to top, with each layer training on the “output” or activations of
the hidden units of the layer below. After the last layer is pretrained, a supervised
layer is put on top, and all the layers are jointly trained with respect to the overall
supervised training criterion. In other words, the pre-training was only used to
initialize a deep supervised neural network (which could be a convolutional neural
network (Ranzato et al., 2007)). This is illustrated in Figure 17.1.
However, greedy layerwise unsupervised pre-training can also be used as ini-
tialization for other unsupervised learning algorithms, such as deep auto-encoders (Hin-
ton and Salakhutdinov, 2006), deep belief networks (Hinton et al., 2006) (Sec-
tion 21.4), or deep Boltzmann machines (Salakhutdinov and Hinton, 2009a) (Sec-
tion 21.5).
As discussed in Section 8.6.4, it is also possible to have greedy layerwise su-
pervised pre-training, to help optimize deep supervised networks. This builds on
the premise that training a shallow network is easier than training a deep one,
which seems to have been validated in several contexts (Erhan et al., 2010).
17.1.1 Why Does Unsupervised Pre-Training Work?
What has been observed on several datasets starting in 2006 (Hinton et al., 2006;
Bengio et al., 2007; Ranzato et al., 2007) is that greedy layer-wise unsupervised
pre-training can yield substantial improvements in test error for classification
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Algorithm 17.1 Greedy layer-wise unsupervised pre-training protocol.
Given the following: Unsupervised feature learner L, which takes a training set
D of examples and returns an encoder or feature function f = L(D). The raw
input data is X, with one row per example and f(X) is the dataset used by the
second level unsupervised feature learner. In the case fine-tuning is performed, we
use a learner T which takes an initial function f, input examples X (and in the
supervised fine-tuning case, associated targets Y ), and returns a tuned function.
The number of stages is M.
D
(0)
= X
f ← Identity function
for k = 1 . . . , M do
f
(k)
= L(D)
f ← f
(k)
◦ f
end for
if fine-tuning then
f ← T (f, X, Y )
end if
Return f
Figure 17.1: Illustration of the greedy layer-wise unsupervised pre-training scheme, in
the case of a network with 3 hidden layers. The protocol proceeds in 4 phases (one per
hidden layer, plus the final supervised fine-tuning phase), from left to right. For the
unsupervised steps, each layer (darker grey) is trained to learn a better representation of
the output of the previously trained layer (initially, the raw input). These representations
learned by unsupervised learning form the initialization of a deep supervised net, which
is then trained (fine-tuned) as usual (last phase, right), with all parameters being free to
change (darker grey).
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tasks. Later work suggested that the improvements were less marked (or not even
visible) when very large labeled datasets are available, although the boundary
between the two behaviors remains to be clarified, i.e., it may not just be an issue
of number of labeled examples but also how this relates to the complexity of the
function to be learned.
A question that thus naturally arises is the following: why and when does
unsupervised pre-training work? Although previous studies have mostly focused
on the case when the final task is supervised (with supervised fine-tuning), it is
also interesting to keep in mind that one gets improvements in terms of both
training and test performance in the case of unsupervised fine-tuning, e.g., when
training deep auto-encoders (Hinton and Salakhutdinov, 2006).
This “why does it work” question is at the center of the paper by Erhan et al.
(2010), and their experiments focused on the supervised fine-tuning case. They
consider different machine learning hypotheses to explain the results observed,
and attempted to confirm those via experiments. We summarize some of this
investigation here.
First of all, they studied the trajectories of neural networks during supervised
fine-tuning, and evaluated how different they were depending on initial conditions,
i.e., due to random initialization or due to performing unsupervised pre-training
or not. The main result is illustrated and discussed in Figures 17.2 and 17.2.
Note that it would not make sense to plot the evolution of parameters of these
networks directly, because the same input-to-output function can be represented
by different parameter values (e.g., by relabeling the hidden units). Instead,
this work plots the trajectories in function space, by considering the output of a
network (the class probability predictions) for a given set of test examples as a
proxy for the function computed. By concatenating all these outputs (over say
1000 examples) and doing dimensionality reduction on these vectors, we obtain
the kinds of plots illustrated in the figure.
The main conclusions of these kinds of plots are the following:
1. Each training trajectory goes to a different place, i.e., different trajectories
do not converge to the same place. These “places” might be in the vicinity of
a local minimum or as we understand it better now (Dauphin et al., 2014)
these are more likely to be an “apparent local minimum” in the region
of flat derivatives near a saddle point. This suggests that the number of
these apparent local minima is huge, and this also is in agreement with
theory (Dauphin et al., 2014; Choromanska et al., 2014).
2. Depending on whether we initialize with unsupervised pre-training or not,
very different functions (in function space) are obtained, covering regions
that do not overlap. Hence there is a qualitative effect due to unsupervised
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Figure 17.2: Illustrations of the trajectories of different neural networks in function space
(not parameter space, to avoid the issue of many-to-one mapping from parameter vector
to function), with different random initializations and with or without unsupervised pre-
training. Each plus or diamond point corresponds to a different neural network, at a
particular time during its training trajectory, with the function it computes projected to
2-D by t-SNE (van der Maaten and Hinton, 2008a) (this figure) or by Isomap (Tenenbaum
et al., 2000) (Figure 17.3). Color indicates the number of training epochs. What we see
is that no two networks converge to the same function (so a large number of apparent
local minima seems to exist), and that networks initialized with pre-training learn very
different functions, in a region of function space that does not overlap at all with those
learned by networks without pre-training. Such curves were introduced by Erhan et al.
(2010) and are reproduced here with permission.
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Figure 17.3: See Figure 17.2’s caption. This figure only differs in the use of
Isomap (Tenenbaum et al., 2000) rather than t-SNE (van der Maaten and Hinton, 2008b)
for dimensionality reduction. Note that Isomap tries to preserve global relative distances
(and hence volumes), whereas t-SNE only cares about preserving local geometry and
neighborhood relationships. We see with the Isomap dimensionality reduction that the
volume in function space occupied by the networks with pre-training is much smaller (in
fact that volume gets reduced rather than increased, during training), suggesting that
the set of solutions enjoy smaller variance, which would be consistent with the observed
improvements in generalization error. Such curves were introduced by Erhan et al. (2010)
and are reproduced here with permission.
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Figure 17.4: Histograms presenting the test errors obtained on MNIST using denoising
auto-encoder models trained with or without pre-training (400 different initializations
each). Left: 1 hidden layer. Right: 4 hidden layers. We see that the advantage brought
by pre-training increases with depth, both in terms of mean error and in terms of the
variance of the error (w.r.t. random initialization).
TODO: figure credit saying these came from Erhan 2010....
pre-training.
3. With unsupervised pre-training, the region of space covered by the solutions
associated with different initializations shrinks as we consider more training
iterations, whereas it grows without unsupervised pre-training. This is only
apparent in the visualization of Figure 17.3, which attempts to preserve
volume. A larger region is bad for generalization (because not all these
functions can be the right one together), yielding higher variance. This is
consistent with the better generalization observed with unsupervised pre-
training.
Another interesting effect is that the advantage of pre-training seems to in-
crease with depth, as illustrated in Figure 17.4, with both the mean and the
variance of the error decreasing more for deeper networks.
An important question is whether the advantage brought by pre-training can
be seen as a form of regularizer (which could help test error but hurt training error)
or simply a way to find a better minimizer of training error (e.g., by initializing
near a better minimum of training error). The experiments suggest pre-training
actually acts as a regularizer, i.e., hurting training error at least in some cases
(with deeper networks). So if it also helps optimization, it is only because it
initializes closer to a good solution from the point of view of generalization, not
necessarily from the point of view of the training set.
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How could unsupervised pre-training act as regularizer? Simply by imposing
an extra constraint: the learned representations should not only be consistent
with better predicting outputs y but they should also be consistent with better
capturing the variations in the input x, i.e., modeling P (x). This is associated
implicitly with a prior, i.e., that P (y|x) and P (x) share structure, i.e., that
learning about P (x) can help to generalize better on P (y|x). Obviously this needs
not be the case in general, e.g., if y is an effect of x. However, if y is a cause of
x, then we would expect this a priori assumption to be correct, as discussed at
greater length in Section 17.4 in the context of semi-supervised learning.
A disadvantage of unsupervised pre-training is that it is difficult to choose
the capacity hyperparameters (such as when to stop training) for the pre-training
phases. An expensive option is to try many different values of these hyperpa-
rameters and choose the one which gives the best supervised learning error after
fine-tuning. Another potential disadvantage is that unsupervised pre-training
may require larger representations than what would be necessarily strictly for the
task at hand, since presumably, y is only one of the factors that explain x.
Today, as many deep learning researchers and practitioners have moved to
working with very large labeled datasets, unsupervised pre-training has become
less popular in favor of other forms of regularization such as dropout – to be
discussed in section 7.11. Nevertheless, unsupervised pre-training remains an
important tool in the deep learning toolbox and should particularly be considered
when the number of labeled examples is low, such as in the semi-supervised,
domain adaptation and transfer learning settings, discussed next.
17.2 Transfer Learning and Domain Adaptation
Transfer learning and domain adaptation refer to the situation where what has
been learned in one setting (i.e., distribution P
1
) is exploited to improve general-
ization in another setting (say distribution P
2
).
In the case of transfer learning, we consider that the task is different but many
of the factors that explain the variations in P
1
are relevant to the variations that
need to be captured for learning P
2
. This is typically understood in a supervised
learning context, where the input is the same but the target may be of a different
nature, e.g., learn about visual categories that are different in the first and the
second setting. If there is a lot more data in the first setting (sampled from P
1
),
then that may help to learn representations that are useful to quickly generalize
when examples of P
2
are drawn. For example, many visual categories share low-
level notions of edges and visual shapes, the effects of geometric changes, changes
in lighting, etc. In general, transfer learning, multi-task learning (Section 7.12),
and domain adaptation can be achieved via representation learning when there
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exist features that would be useful for the different settings or tasks, i.e., there
are shared underlying factors. This is illustrated in Figure 7.6, with shared lower
layers and task-dependent upper layers.
However, sometimes, what is shared among the different tasks is not the se-
mantics of the input but the semantics of the output, or maybe the input needs
to be treated differently (e.g., consider user adaptation or speaker adaptation).
In that case, it makes more sense to share the upper layers (near the output)
of the neural network, and have a task-specific pre-processing, as illustrated in
Figure 17.5.
h
1
h
2
h
3
Y
X
1
X
2
X
3
selection switch
Figure 17.5: Example of architecture for multi-task or transfer learning when the output
variable Y has the same semantics for all tasks while the input variable X has a different
meaning (and possibly even a different dimension) for each task (or, for example, each
user), called X
1
, X
2
and X
3
for three tasks in the figure. The lower levels (up to the
selection switch) are task-specific, while the upper levels are shared. The lower levels
learn to translate their task-specific input into a generic set of features.
In the related case of domain adaptation, we consider that the task (and the
optimal input-to-output mapping) is the same but the input distribution is slightly
different. For example, if we predict sentiment (positive or negative judgement)
associated with textual comments posted on the web, the first setting may refer to
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consumer comments about books, videos and music, while the second setting may
refer to televisions or other products. One can imagine that there is an underlying
function that tells whether any statement is positive, neutral or negative, but
of course the vocabulary, style, accent, may vary from one domain to another,
making it more difficult to generalize across domains. Simple unsupervised pre-
training (with denoising auto-encoders) has been found to be very successful for
sentiment analysis with domain adaptation (Glorot et al., 2011c).
A related problem is that of concept drift, which we can view as a form of
transfer learning due to gradual changes in the data distribution over time. Both
concept drift and transfer learning can be viewed as particular forms of multi-task
learning (Section 7.12). Whereas multi-task learning is typically considered in
the context of supervised learning, the more general notion of transfer learning is
applicable for unsupervised learning and reinforcement learning as well. Figure 7.6
illustrates an architecture in which different tasks share underlying features or
factors, taken from a larger pool that explain the variations in the input.
In all of these cases, the objective is to take advantage of data from a first
setting to extract information that may be useful when learning or even when
directly making predictions in the second setting. One of the potential advantages
of representation learning for such generalization challenges, and especially of
deep representation learning, is that it may considerably help to generalize by
extracting and disentangling a set of explanatory factors from data of the first
setting, some of which may be relevant to the second setting. In the case of
object recognition from an image, many of the factors of variation that explain
visual categories in natural images remain the same when we move from one set
of categories to another.
This discussion raises a very interesting and important question which is one
of the core questions of this book: what is a good representation? Is it possible to
learn representations that disentangle the underlying factors of variation? This
theme is further explored at the end of this chapter (Section 17.4 and beyond). We
claim that learning the most abstract features helps to maximize our chances of
success in transfer learning, domain adaptation, or concept drift. More abstract
features are more general and more likely to be close to the underlying causal
factor, i.e., be relevant over many domains, many categories, and many time
periods.
A good example of the success of unsupervised deep learning for transfer
learning is its success in two competitions that took place in 2011, with results
presented at ICML 2011 (and IJCNN 2011) in one case (Mesnil et al., 2011) (the
Transfer Learning Challenge, http://www.causality.inf.ethz.ch/unsupervised-learning.php)
and at NIPS 2011 (Goodfellow et al., 2011) in the other case (the Transfer Learn-
ing Challenge that was held as part of the NIPS’2011 workshop on Challenges in
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learning hierarchical models, https://sites.google.com/site/nips2011workshop/transfer-learning-challenge).
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Figure 17.6: Results obtained on the Sylvester validation set (Transfer Learning Chal-
lenge). From left to right and top to bottom, respectively 0, 1, 2, 3, and 4 pre-trained
layers. Horizontal axis is logarithm of number of labeled training examples on transfer
setting (test task). Vertical axis is Area Under the Curve, which reflects classification
accuracy. With deeper representations (learned unsupervised), the learning curves con-
siderably improve, requiring fewer labeled examples to achieve the best generalization.
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In the first of these competitions, the experimental setup is the following.
Each participant is first given a dataset from the first setting (from distribution
P
1
), basically illustrating examples of some set of categories. The participants
must use this to learn a good feature space (mapping the raw input to some
representation), such that when we apply this learned transformation to inputs
from the transfer setting (distribution P
2
), a linear classifier can be trained and
generalize well from very few labeled examples. Figure 17.6 illustrates one of the
most striking results: as we consider deeper and deeper representations (learned
in a purely unsupervised way from data of the first setting P
1
), the learning curve
on the new categories of the second (transfer) setting P
2
becomes much better, i.e.,
less labeled examples of the transfer tasks are necessary to achieve the apparently
asymptotic generalization performance.
An extreme form of transfer learning is one-shot learning or even zero-shot
learning or zero-data learning, where one or even zero example of the new task
are given.
One-shot learning (Fei-Fei et al., 2006) is possible because, in the learned
representation, the new task corresponds to a very simple region, such as a ball-
like region or the region around a corner of the space (in a high dimensional
space, there are exponentially many corners). This works to the extent that the
factors of variation corresponding to these invariances have been cleanly separated
from other factors, in the learned representation space, and we have somehow
learned which factors do and do not matter when discriminating objects of certain
categories.
input x
task specification t
output f
t
(x)
Figure 17.7: Figure illustrating how zero-data or zero-shot learning is possible. The trick
is that the new context or task on which no example is given but on which we want a
prediction is represented (with an input t), e.g., with a set of features, i.e., a distributed
representation, and that representation is used by the predictor f
t
(x). If t was a one-hot
vector for each task, then it would not be possible to generalize to a new task, but with
a distributed representation the learner can benefit from the meaning of the individual
task features (as they influence the relationship between inputs x and targets y, say),
learned on other tasks for which examples are available.
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Zero-data learning (Larochelle et al., 2008) and zero-shot learning (Richard Socher
and Ng, 2013) are only possible because additional information has been exploited
during training that provides representations of the “task” or “context”, helping
the learner figure out what is expected, even though no example of the new task
has ever been seen. For example, in a multi-task learning setting, if each task
is associated with a set of features, i.e., a distributed representation (that is al-
ways provided as an extra input, in addition to the ordinary input associated
with the task), then one can generalize to new tasks based on the similarity be-
tween the new task and the old tasks, as illustrated in Figure 17.7. One learns
a parametrized function from inputs to outputs, parametrized by the task repre-
sentation. In the case of zero-shot learning (Richard Socher and Ng, 2013), the
“task” is a representation of a semantic object (such as a word), and its repre-
sentation has already been learned from data relating different semantic objects
together (such as natural language data, relating words together). On the other
hand, for some of the tasks (e.g., words) one has data associating the variables
of interest (e.g., words, pixels in images). Thus one can generalize and associate
images to words for which no labeled images were previously shown to the learner.
A similar phenomenon happens in machine translation (Klementiev et al., 2012;
Mikolov et al., 2013; Gouws et al., 2014): we have words in one language, and
the relationships between words can be learned from unilingual corpora; on the
other hand, we have translated sentences which relate words in one language with
words in the other. Even though we may not have labeled examples translating
word A in language X to word B in language Y, we can generalize and guess a
translation for word A because we have learned a distributed representation for
words in language X, a distributed representation for words in language Y, and
created a link (possibly two-way) relating the two spaces, via translation exam-
ples. Note that this transfer will be most successful if all three ingredients (the
two representations and the relations between them) are learned jointly.
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X
Y
Figure 17.8: Transfer learning between two domains corresponds to zero-shot learning.
A first set of data (dashed arrows) can be used to relate examples in one domain (top
left, X) and fix a relationship between their representations, a second set of data (dotted
arrows) can be used to similarly relate examples and their representation in the other
domain (bottom right, Y ), while a third dataset (full large arrows) anchors the two
representations together, with examples consisting of pairs (x, y) taken from the two
domains. In this way, one can for example associate an image to a word, even if no
images of that word were ever presented, simply because word-representations (top) and
image-representations (bottom) have been learned jointly with a two-way relationship
between them.
This is illustrated in Figure 17.8, where we see that zero-shot learning is a
particular form of transfer learning. The same principle explains how one can
perform multi-modal learning, capturing a representation in one modality, a rep-
resentation in the other, and the relationship (in general a joint distribution)
between pairs (x, y) consisting of one observation x in one modality and another
observation y in the other modality (Srivastava and Salakhutdinov, 2012). By
learning all three sets of parameters (from x to its representation, from y to its
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representation, and the relationship between the two representation), concepts in
one map are anchored in the other, and vice-versa, allowing one to meaningfully
generalize to new pairs.
17.3 Semi-Supervised Learning
As discussed in Section 17.1.1 on the advantages of unsupervised pre-training, un-
supervised learning can have a regularization effect in the context of supervised
learning. This fits in the more general category of combining unlabeled examples
with unknown distribution P (x) with labeled examples (x, y), with the objective
of estimating P(y|x). Exploiting unlabeled examples to improve performance
on a labeled set is the driving idea behind semi-supervised learning (Chapelle
et al., 2006). For example, one can use unsupervised learning to map X into a
representation (also called embedding) such that two examples x
1
and x
2
that
belong to the same cluster (or are reachable through a short path going through
neighboring examples in the training set) end up having nearby embeddings. One
can then use supervised learning (e.g., a linear classifier) in that new space and
achieve better generalization in many cases (Belkin and Niyogi, 2002; Chapelle
et al., 2003). A long-standing variant of this approach is the application of Prin-
cipal Components Analysis as a pre-processing step before applying a classifier
(on the projected data). In these models, the data is first transformed in a new
representation using unsupervised learning, and a supervised classifier is stacked
on top, learning to map the data in this new representation into class predictions.
Instead of having separate unsupervised and supervised components in the
model, one can consider models in which P (x) (or P (x, y)) and P (y|x) share
parameters (or whose parameters are connected in some way), and one can trade-
off the supervised criterion −log P (y|x) with the unsupervised or generative one
(−log P (x) or −log P (x, y)). It can then be seen that the generative criterion
corresponds to a particular form of prior (Lasserre et al., 2006), namely that the
structure of P (x) is connected to the structure of P (y|x) in a way that is cap-
tured by the shared parametrization. By controlling how much of the generative
criterion is included in the total criterion, one can find a better trade-off than
with a purely generative or a purely discriminative training criterion (Lasserre
et al., 2006; Larochelle and Bengio, 2008b).
In the context of deep architectures, a very interesting application of these
ideas involves adding an unsupervised embedding criterion at each layer (or only
one intermediate layer) to a traditional supervised criterion (Weston et al., 2008).
This has been shown to be a powerful semi-supervised learning strategy, and is
an alternative to the unsupervised pre-training approach described earlier in this
chapter, which also combine unsupervised learning with supervised learning.
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In the context of scarcity of labeled data (and abundance of unlabeled data),
deep architectures have shown promise as well. Salakhutdinov and Hinton (2008)
describe a method for learning the covariance matrix of a Gaussian Process, in
which the usage of unlabeled examples for modeling P (x) improves P (y|x) quite
significantly. Note that such a result is to be expected: with few labeled sam-
ples, modeling P (x) usually helps, as argued below (Section 17.4). These results
show that even in the context of abundant labeled data, unsupervised pre-training
can have a pronounced positive effect on generalization: a somewhat surprising
conclusion.
17.4 Semi-Supervised Learning and Disentangling Un-
derlying Causal Factors
What we put forward as a hypothesis, going a bit further, is that an ideal repre-
sentation is one that disentangles the underlying causal factors of variation that
generated the observed data. Note that this may be different from “easy to model”,
but we further assume that for most problems of interest, these two properties
coincide: once we “understand” the underlying explanations for what we observe,
it generally becomes easy to predict one thing from others.
A very basic question is whether unsupervised learning on input variables x
can yield representations that are useful when later trying to learn to predict some
target variable y, given x. More generally, when does semi-supervised learning
work? See also Section 17.3 for an earlier discussion.
It turns out that the answer to this question is very different dependent on the
underlying relationship between x and y. Put differently, the question is whether
P (y|x), seen as a function of x has anything to do with P (x). If not, then
unsupervised learning of P (x) can be of no help to learn P (y|x). Consider for
example the case where P (x) is uniformly distributed and E[y|x] is some function
of interest. Clearly, observing x alone gives us no information about P (y|x). As
a better case, consider the situation where x arises from a mixture, with one
mixture component per value of y, as illustrated in Figure 17.9. If the mixture
components are well separated, then modeling P (x) tells us precisely where each
component is, and a single labeled example of each example will then be enough
to perfectly learn P (y|x). But more generally, what could make P (y|x) and P (x)
tied together?
If y is closely associated with one of the causal factors of x, then, as first argued
by Janzing et al. (2012), P (x) and P (y|x) will be strongly tied, and unsupervised
representation learning that tries to disentangle the underlying factors of variation
is likely to be useful as a semi-supervised learning strategy.
Consider the assumption that y is one of the causal factors of x, and let h
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y =1
y =2
y =2
P (x)
x
Figure 17.9: Example of a density over x that is a mixture over three components. The
component identity is an underlying explanatory factor, y. Because the mixture compo-
nents (e.g., natural object classes in image data) are statistically salient, just modeling
P (x) in an unsupervised way with no labeled example already reveals the factor y.
represent all those factors. Then the true generative process can be conceived as
structured according to this directed graphical model, with h as the parent of x:
P (h, x) = P (x|h)P (h).
As a consequence, the data has marginal probability
P (x) =
Z
P (x|h)p(h)dh
or, in the discrete case (like in the mixture example above):
P (x) =
X
h
P (x|h)P (h).
From this straightforward observation, we conclude that the best possible model
of x (from a generalization point of view) is the one that uncovers the above
“true” structure, with h as a latent variable that explains the observed variations
in x. The “ideal” representation learning discussed above should thus recover
these latent factors. If y is one of them (or closely related to one of them), then
it will be very easy to learn to predict y from such a representation. We also
see that the conditional distribution of y given x is tied by Bayes rule to the
components in the above equation:
P (y|x) =
P (x|y)P (y)
P (x)
.
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Thus the marginal P (x) is intimately tied to the conditional P (y|x) and knowl-
edge of the structure of the former should be helpful to learn the latter, i.e.,
semi-supervised learning works. Furthermore, not knowing which of the factors
in h will be the one of the interest, say y = h
i
, an unsupervised learner should
learn a representation that disentangles all the generative factors h
j
from each
other, then making it easy to predict y from h.
In addition, as pointed out by Janzing et al. (2012), if the true generative
process has x as an effect and y as a cause, then modeling P (x|y) is robust to
changes in P (y). If the cause-effect relationship was reversed, it would not be true,
since by Bayes rule, P (x|y) would be sensitive to changes in P (y). Very often,
when we consider changes in distribution due to different domains, temporal non-
stationarity, or changes in the nature of the task, the causal mechanisms remain
invariant (“the laws of the universe are constant”) whereas what changes are the
marginal distribution over the underlying causes (or what factors are linked to
our particular task). Hence, better generalization and robustness to all kinds of
changes can be expected via learning a generative model that attempts to recover
the causal factors h and P (x|h).
17.5 Assumption of Underlying Factors and Distributed
Representation
A very basic notion that comes out of the above discussion and of the notion
of “disentangled factors” is the very idea that there are underlying factors that
generate the observed data. It is a core assumption behind most neural network
and deep learning research, more precisely relying on the notion of distributed
representation.
What we call a distributed representation is one which can express an ex-
ponentially large number of concepts by allowing to compose the activation of
many features. An example of distributed representation is a vector of n binary
features, which can take 2
n
configurations, each potentially corresponding to a
different region in input space. This can be compared with a symbolic represen-
tation, where the input is associated with a single symbol or category. If there
are n symbols in the dictionary, one can imagine n feature detectors, each corre-
sponding to the detection of the presence of the associated category. In that case
only n different configurations of the representation-space are possible, carving n
different regions in input space. Such a symbolic representation is also called a
one-hot representation, since it can be captured by a binary vector with n bits
that are mutually exclusive (only one of them can be active). These ideas are
developed further in the next section.
Examples of learning algorithms based on non-distributed representations in-
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clude:
• Clustering methods, including the k-means algorithm: only one cluster
“wins” the competition.
• K-nearest neighbors algorithms: only one template or prototype example is
associated with a given input.
• Decision trees: only one leaf (and the nodes on the path from root to leaf)
is activated when an input is given.
• Gaussian mixtures and mixtures of experts: the templates (cluster centers)
or experts are now associated with a degree of activation, which makes the
posterior probability of components (or experts) given input look more like
a distributed representation. However, as discussed in the next section,
these models still suffer from a poor statistical scaling behavior compared
to those based on distributed representations (such as products of experts
and RBMs).
• Kernel machines with a Gaussian kernel (or other similarly local kernel): al-
though the degree of activtion of each “support vector” or template example
is now continuous-valued, the same issue arises as with Gaussian mixtures.
• Language or translation models based on N-grams: the set of contexts (se-
quences of symbols) is partitioned according to a tree structure of suffixes
(e.g. a leaf may correspond to the last two words being w
1
and w
2
), and
separate parameters are estimated for each leaf of the tree (with some shar-
ing being possible of parameters associated with internal nodes, between
the leaves of the sub-tree rooted at the same internal node).
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CHAPTER 17. REPRESENTATION LEARNING
Figure 17.10: Illustration of how a learning algorithm based on a non-distributed repre-
sentation breaks up the input space into regions, with a separate set of parameters for
each region. For example, a clustering algorithm or a 1-nearest-neighbor algorithm asso-
ciates one template (colored X) to each region. This is also true of decision trees, mixture
models, and kernel machines with a local (e.g., Gaussian) kernel. In the latter algorithms,
the output is not constant by parts but instead interpolates between neighboring regions,
but the relationship between the number of parameters (or examples) and the number
of regions they can define remains linear. The advantage is that a different answer (e.g.,
density function, predicted output, etc.) can be independently chosen for each region.
The disadvantage is that there is no generalization to new regions, except by extending
the answer for which there is data, exploiting solely a smoothness prior. It makes it
difficult to learn a complicated function, with more ups and downs than the available
number of examples. Contrast this with a distributed representation, Figure 17.11.
An important related concept that distinguishes a distributed representation
from a symbolic one is that generalization arises due to shared attributes between
different concepts. As pure symbols, “ tt cat” and “dog” are as far from each
other as any other two symbols. However, if one associates them with a meaningful
distributed representation, then many of the things that can be said about cats
can generalize to dogs and vice-versa. This is what allows neural language models
to generalize so well (Section 13.3). Distributed representations induce a rich
similarity space, in which semantically close concepts (or inputs) are close in
distance, a property that is absent from purely symbolic representations. Of
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course, one would get a distributed representation if one would associated multiple
symbolic attributes to each symbol.
Figure 17.11: Illustration of how a learning algorithm based on a distributed repre-
sentation breaks up the input space into regions, with exponentially more regions than
parameters. Instead of a single partition (as in the non-distributed case, Figure 17.10),
we have many partitions, one per “feature”, and all their possible intersections. In the
example of the figure, there are 3 binary features C1, C2, and C3, each corresponding to
partitioning the input space in two regions according to a hyperplane, i.e., each is a linear
classifier. Each possible intersection of these half-planes forms a region, i.e., each region
corresponds to a configuration of the bits specifying whether each feature is 0 or 1, on
which side of their hyperplane is the input falling. If the input space is large enough, the
number of regions grows exponentially with the number of features, i.e., of parameters.
However, the way these regions carve the input space still depends on few parameters:
this huge number of regions are not placed independently of each other. We can thus
represent a function that looks complicated but actually has structure. Basically, the as-
sumption is that one can learn about each feature without having to see the examples for
all the configurations of all the other features, i.e., these features corespond to underlying
factors explaining the data.
Note that a sparse representation is a distributed representation where the
number of attributes that are active together is small compared to the total num-
ber of attributes. For example, in the case of binary representations, one might
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have only k n of the n bits that are non-zero. The power of representation
grows exponentially with the number of active attributes, e.g., O(n
k
) in the above
example of binary vectors. At the extreme, a symbolic representation is a very
sparse representation where only one attribute at a time can be active.
17.6 Exponential Gain in Representational Efficiency
from Distributed Representations
When and why can there be a statistical advantage from using a distributed
representation as part of a learning algorithm?
Figures 17.10 and 17.11 explain that advantage in intuitive terms. The argu-
ment is that a function that “looks complicated” can be compactly represented
using a small number of parameters, if some “structure” is uncovered by the
learner. Traditional “non-distributed” learning algorithms generalize only due to
the smoothness assumption, which states that if u ≈ v, then the target function f
to be learned has the property that f(u) ≈ f(v), in general. There are many ways
of formalizing such an assumption, but the end result is that if we have an exam-
ple (x, y) for which we know that f(x) ≈ y, then we choose an estimator
ˆ
f that
approximately satisfies these constraints while changing as little as possible. This
assumption is clearly very useful, but it suffers from the curse of dimensionality:
in order to learn a target function that takes many different values (e.g. many
ups and downs) in a large number of regions
2
, we may need a number of examples
that is at least as large as the number of distinguishible regions. One can think
of each of these regions as a category or symbol: by having a separate degree
of freedom for each symbol (or region), we can learn an arbitrary mapping from
symbol to value. However, this does not allow us to generalize to new symbols,
new regions.
If we are lucky, there may be some regularity in the target function, besides
being smooth. For example, the same pattern of variation may repeat itself many
times (e.g., as in a periodic function or a checkerboard). If we only use the
smoothness prior, we will need additional examples for each repetition of that
pattern. However, as discussed by Montufar et al. (2014), a deep architecture
could represent and discover such a repetition pattern and generalize to new in-
stances of it. Thus a small number of parameters (and therefore, a small number
of examples) could suffice to represent a function that looks complicated (in the
sense that it would be expensive to represent with a non-distributed architec-
ture). Figure 17.11 shows a simple example, where we have n binary features
2
e.g., exponentially many regions: in a d-dimensional space with at least 2 different values
to distinguish per dimension, we might want f to differ in 2
d
different regions, requiring O(2
d
)
training examples.
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CHAPTER 17. REPRESENTATION LEARNING
in a d-dimensional space, and where each binary feature corresponds to a linear
classifier that splits the input space in two parts. The exponentially large number
of intersections of n of the corresponding half-spaces corresponds to as many dis-
tinguishable regions that a distributed representation learner could capture. How
many regions are generated by an arrangement of n hyperplanes in R
d
? This
corresponds to the number of regions that a shallow neural network (one hidden
layer) can distinguish (Pascanu et al., 2014c), which is
d
X
j=0
n
j
= O(n
d
),
following a more general result from Zaslavsky (1975), known as Zaslavsky’s the-
orem, one of the central results from the theory of hyperplane arrangements.
Therefore, we see a growth that is exponential in the input size and polynomial
in the number of hidden units.
Although a distributed representation (e.g. a shallow neural net) can represent
a richer function with a smaller number of parameters, there is no free lunch: to
construct an arbitrary partition (say with 2
d
different regions) one will need a
correspondingly large number of hidden units, i.e., of parameters and of examples.
The use of a distributed representation therefore also corresponds to a prior, which
comes on top of the smoothness prior. To return to the hyperplanes examples of
Figure 17.11, we see that we are able to get this generalization because we can
learn about the location of each hyperplane with only O(d) examples: we do not
need to see examples corresponding to all O(n
d
) regions.
Let us consider a concrete example. Imagine that the input is the image of a
person, and that we have a classifier that detects whether the person is a child or
not, another that detects if that person is a male or a female, another that detects
whether that person wears glasses or not, etc. Keep in mind that these features are
discovered automatically, not fixed a priori. We can learn about the male vs female
distinction, or about the glasses vs no-classes case, without having to consider all
of the configurations of the n features. This form of statistical separability is
what allows one to generalize to new configurations of a person’s features that
have never been seen during training. It corresponds to the prior discussed above
regarding the existence of multiple underlying explanatory factors. This prior
is very plausible for most of the data distributions on which human intelligence
would be useful, but it may not apply to every possible distribution. However, this
apparently innocuous assumption buys us a lot, statistically speaking, because
it allows the learner to discover structure with a reasonably small number of
examples that would otherwise require exponentially more training data.
Another interesting result illustrating the statistical effect of a distributed rep-
resentations versus a non-distributed one is the mathematical analysis (Montufar
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CHAPTER 17. REPRESENTATION LEARNING
and Morton, 2014) of products of mixtures (which include the RBM as a special
case) versus mixture of products (such as the mixture of Gaussians). The analy-
sis shows that a mixture of products can require an exponentially larger number
of parameters in order to represent the probability distributions arising out of a
product of mixtures.
17.7 Exponential Gain in Representational Efficiency
from Depth
In the above example with the input being an image of a person, it would not
be reasonable to expect factors such as gender, age, and the presence of glasses
to be detected simply from a linear classifier, i.e., a shallow neural network. The
kinds of factors that can be chosen almost independently in order to generate
data are more likely to be very high-level and related in highly non-linear ways to
the input. This demands deep distributed representations, where the higher level
features (seen as functions of the input) or factors (seen as generative causes) are
obtained through the composition of many non-linearities.
It turns out that organizing computation through the composition of many
non-linearities and a hierarchy of reused features can give another exponential
boost to statistical efficiency. Although 2-layer networks (e.g., with saturating
non-linearities, boolean gates, sum/products, or RBF units) can generally be
shown to be universal approximators
3
, the required number of hidden units may
be very large. The main results on the expressive power of deep architectures
state that there are families of functions that can be represented efficiently with
a deep architecture (say depth k) but would require an exponential number of
components (with respect to the input size) with insufficient depth (depth 2 or
depth k − 1).
More precisely, a feedforward neural network with a single hidden layer is
a universal approximator (of Borel measurable functions) (Hornik et al., 1989;
Cybenko, 1989). Other works have investigated universal approximation of prob-
ability distributions by deep belief networks (Le Roux and Bengio, 2010; Mont´ufar
and Ay, 2011), as well as their approximation properties (Mont´ufar, 2014; Krause
et al., 2013).
Regarding the advantage of depth, early theoretical results have focused on
circuit operations (neural net unit computations) that are substantially differ-
ent from those being used in real state-of-the-art deep learning applications,
such as logic gates (H˚astad, 1986) and linear threshold units with non-negative
weights (H˚astad and Goldmann, 1991). More recently, Delalleau and Bengio
3
with enough hidden units they can approximate a large class of functions (e.g. continuous
functions) up to some given tolerance level
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CHAPTER 17. REPRESENTATION LEARNING
Figure 17.12: A sum-product network (Poon and Domingos, 2011) composes summing
units and product units, so that each node computes a polynomial. Consider the product
node computing x
2
x
3
: its value is reused in its two immediate children, and indirectly
incorporated in its grand-children. In particular, in the top node shown the product
x
2
x
3
would arise 4 times if that node’s polynomial was expanded as a sum of products.
That number could double for each additional layer. In general a deep sum of product
can represent polynomials with a number of min-terms that is exponential in depth,
and some families of polynomials are represented efficiently with a deep sum-product
network but not efficiently representable with a simple sum of products, i.e., a 2-layer
network (Delalleau and Bengio, 2011).
(2011) showed that a shallow network requires exponentially many more sum-
product hidden units
4
than a deep sum-product network (Poon and Domingos,
2011) in order to compute certain families of polynomials. Figure 17.12 illus-
trates a sum-product network for representing polynomials, and how a deeper
network can be exponentially more efficient because the same computation can
be reused exponentially (in depth) many times. Note however that Martens and
Medabalimi (2014) showed that sum-product networks may be have limitations
in their expressive power, in the sense that there are distributions that can easily
be represented by other generative models but that cannot be efficiently repre-
sented under the decomposability and completeness conditions associated with
the probabilistic interpretation of sum-product networks (Poon and Domingos,
2011).
Closer to the kinds of deep networks actually used in practice (Pascanu et al.,
2014a; Montufar et al., 2014) showed that piecewise linear networks (e.g. ob-
tained from rectifier non-linearities or maxout units) could represent functions
4
Here, a single sum-product hidden layer summarizes a layer of product units followed by a
layer of sum units.
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CHAPTER 17. REPRESENTATION LEARNING
Figure 17.13: An absolute value rectification unit has the same output for every pair
of mirror points in its input. The mirror axis of symmetry is given by the hyperplane
defined by the weights and bias of the unit. If one considers a function computed on
top of that unit (the green decision surface), it will be formed of a mirror image of a
simpler pattern, across that axis of symmetry. The middle image shows how it can be
obtained by folding the space around that axis of symmetry, and the right image shows
how another repeating pattern can be folded on top of it (by another downstream unit)
to obtain another symmetry (which is now repeated four times, with two hidden layers).
This is an intuitive explanation of the exponential advantage of deeper rectifier networks
formally shown in Pascanu et al. (2014a); Montufar et al. (2014).
with exponentially more piecewise-linear regions, as a function of depth, com-
pared to shallow neural networks. Figure 17.13 illustrates how a network with
absolute value rectification creates mirror images of the function computed on top
of some hidden unit, with respect to the input of that hidden unit. Each hidden
unit specifies where to fold the input space in order to create mirror responses
(on both sides of the absolute value non-linearity). By composing these folding
operations, we obtain an exponentially large number of piecewise linear regions
which can capture all kinds of regular (e.g. repeating) patterns.
More precisely, the main theorem in Montufar et al. (2014) states that the
number of linear regions carved out by a deep rectifier network with d inputs,
depth L, and n units per hidden layer, is
O
n
d
d(L−1)
n
d
!
,
i.e., exponential in the depth L. In the case of maxout networks with k filters per
unit, the number of linear regions is
O
k
(L−1)+d
.
17.8 Priors Regarding The Underlying Factors
To close this chapter, we come back to the original question: what is a good
representation? We proposed that an ideal representation is one that disentangles
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the underlying causal factors of variation that generated the data, especially those
factors that we care about in our applications. It seems clear that if we have direct
clues about these factors (like if a factor y = h
i
, a label, is observed at the same
time as an input x), then this can help the learner separate these observed factors
from the others. This is already what supervised learning does. But in general,
we may have a lot more unlabeled data than labeled data: can we use other
clues, other hints about the underlying factors, in order to disentangle them more
easily?
What we propose here is that indeed we can provide all kinds of broad priors
which are as many hints that can help the learner discover, identify and disen-
tangle these factors. The list of such priors is clearly not exhaustive, but it is a
starting point, and yet most learning algorithms in the machine learning litera-
ture only exploit a small subset of these priors. With absolutely no priors, we
know that it is not possible to generalize: this is the essence of the no-free-lunch
theorem for machine learning. In the space of all functions, which is huge, with
any finite training set, there is no general-purpose learning recipe that would
dominate all other learning algorithms. Whereas some assumptions are required,
when our goal is to build AI or understand human intelligence, it is tempting to
focus our attention on the most general and broad priors, that are relevant for
most of the tasks that humans are able to successfully learn.
This list was introduced in section 3.1 of Bengio et al. (2013c).
• Smoothness: we want to learn functions f s.t. x ≈ y generally implies
f(x) ≈ f(y). This is the most basic prior and is present in most machine
learning, but is insufficient to get around the curse of dimensionality, as
discussed abov and in Bengio et al. (2013c). below.
• Multiple explanatory factors: the data generating distribution is gener-
ated by different underlying factors, and for the most part what one learns
about one factor generalizes in many configurations of the other factors.
This assumption is behind the idea of distributed representations, dis-
cussed in Section 17.5 above.
• Depth, or a hierarchical organization of explanatory factors: the
concepts that are useful at describing the world around us can be defined in
terms of other concepts, in a hierarchy, with more abstract concepts higher
in the hierarchy, being defined in terms of less abstract ones. This is the
assumption exploited by having deep representations.
• Causal factors: the input variables x are consequences, effects, while the
explanatory factors are causes, and not vice-versa. As discussed above,
this enables the semi-supervised learning assumption, i.e., that P (x)
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CHAPTER 17. REPRESENTATION LEARNING
is tied to P (y|x), making it possible to improve the learning of P (y|x)
via the learning of P (x). More precisely, this entails that representations
that are useful for P(x) are useful when learning P (y|x), allowing sharing of
statistical strength between the unsupervised and supervised learning tasks.
• Shared factors across tasks: in the context where we have many tasks,
corresponding to different y
i
’s sharing the same input x or where each task
is associated with a subset or a function f
i
(x) of a global input x, the as-
sumption is that each y
i
is associated with a different subset from a common
pool of relevant factors h. Because these subsets overlap, learning all the
P (y
i
|x) via a shared intermediate representation P (h|x) allows sharing of
statistical strength between the tasks.
• Manifolds: probability mass concentrates, and the regions in which it con-
centrates are locally connected and occupy a tiny volume. In the continuous
case, these regions can be approximated by low-dimensional manifolds that
a much smaller dimensionality than the original space where the data lives.
This is the manifold hypothesis and is covered in Chapter 18, especially
with algorithms related to auto-encoders.
• Natural clustering: different values of categorical variables such as object
classes
5
are associated with separate manifolds. More precisely, the local
variations on the manifold tend to preserve the value of a category, and a
linear interpolation between examples of different classes in general involves
going through a low density region, i.e., P (x|y = i) for different i tend to
be well separated and not overlap much. For example, this is exploited
explicitly in the Manifold Tangent Classifier discussed in Section 18.5. This
hypothesis is consistent with the idea that humans have named categories
and classes because of such statistical structure (discovered by their brain
and propagated by their culture), and machine learning tasks often involves
predicting such categorical variables.
• Temporal and spatial coherence: this is similar to the cluster assump-
tion but concerns sequences or tuples of observations; consecutive or spa-
tially nearby observations tend to be associated with the same value of
relevant categorical concepts, or result in a small move on the surface of
the high-density manifold. More generally, different factors change at dif-
ferent temporal and spatial scales, and many categorical concepts of inter-
est change slowly. When attempting to capture such categorical variables,
this prior can be enforced by making the associated representations slowly
5
it is often the case that the y of interest is a category
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CHAPTER 17. REPRESENTATION LEARNING
changing, i.e., penalizing changes in values over time or space. This prior
was introduced in Becker and Hinton (1992).
• Sparsity: for any given observation x, only a small fraction of the possible
factors are relevant. In terms of representation, this could be represented
by features that are often zero (as initially proposed by Olshausen and Field
(1996)), or by the fact that most of the extracted features are insensitive
to small variations of x. This can be achieved with certain forms of priors
on latent variables (peaked at 0), or by using a non-linearity whose value is
often flat at 0 (i.e., 0 and with a 0 derivative), or simply by penalizing the
magnitude of the Jacobian matrix (of derivatives) of the function mapping
input to representation. This is discussed in Section 16.7.
• Simplicity of Factor Dependencies: in good high-level representations,
the factors are related to each other through simple dependencies. The
simplest possible is marginal independence, P (h) =
Q
i
P (h
i
), but linear
dependencies or those captured by a shallow auto-encoder are also reason-
able assumptions. This can be seen in many laws of physics, and is assumed
when plugging a linear predictor or a factorized prior on top of a learned
representation.
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