3,944 research outputs found
A Theoretical Analysis of Contrastive Unsupervised Representation Learning
Recent empirical works have successfully used unlabeled data to learn feature
representations that are broadly useful in downstream classification tasks.
Several of these methods are reminiscent of the well-known word2vec embedding
algorithm: leveraging availability of pairs of semantically "similar" data
points and "negative samples," the learner forces the inner product of
representations of similar pairs with each other to be higher on average than
with negative samples. The current paper uses the term contrastive learning for
such algorithms and presents a theoretical framework for analyzing them by
introducing latent classes and hypothesizing that semantically similar points
are sampled from the same latent class. This framework allows us to show
provable guarantees on the performance of the learned representations on the
average classification task that is comprised of a subset of the same set of
latent classes. Our generalization bound also shows that learned
representations can reduce (labeled) sample complexity on downstream tasks. We
conduct controlled experiments in both the text and image domains to support
the theory.Comment: 19 pages, 5 figure
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors
with overlapping, global features. Each input's latent representation is
predicted conditional on the observable data using a feature-rich conditional
random field. Then a reconstruction of the input is (re)generated, conditional
on the latent structure, using models for which maximum likelihood estimation
has a closed-form. Our autoencoder formulation enables efficient learning
without making unrealistic independence assumptions or restricting the kinds of
features that can be used. We illustrate insightful connections to traditional
autoencoders, posterior regularization and multi-view learning. We show
competitive results with instantiations of the model for two canonical NLP
tasks: part-of-speech induction and bitext word alignment, and show that
training our model can be substantially more efficient than comparable
feature-rich baselines
Representation Learning: A Review and New Perspectives
The success of machine learning algorithms generally depends on data
representation, and we hypothesize that this is because different
representations can entangle and hide more or less the different explanatory
factors of variation behind the data. Although specific domain knowledge can be
used to help design representations, learning with generic priors can also be
used, and the quest for AI is motivating the design of more powerful
representation-learning algorithms implementing such priors. This paper reviews
recent work in the area of unsupervised feature learning and deep learning,
covering advances in probabilistic models, auto-encoders, manifold learning,
and deep networks. This motivates longer-term unanswered questions about the
appropriate objectives for learning good representations, for computing
representations (i.e., inference), and the geometrical connections between
representation learning, density estimation and manifold learning
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