• Publications
  • Influence
Domain-Adversarial Training of Neural Networks
TLDR
A new representation learning approach for domain adaptation, in which data at training and test time come from similar but different distributions, which can be achieved in almost any feed-forward model by augmenting it with few standard layers and a new gradient reversal layer. Expand
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
TLDR
This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations. Expand
Extracting and composing robust features with denoising autoencoders
TLDR
This work introduces and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representations robust to partial corruption of the input pattern. Expand
Practical Bayesian Optimization of Machine Learning Algorithms
TLDR
This work describes new algorithms that take into account the variable cost of learning algorithm experiments and that can leverage the presence of multiple cores for parallel experimentation and shows that these proposed algorithms improve on previous automatic procedures and can reach or surpass human expert-level optimization for many algorithms. Expand
Optimization as a Model for Few-Shot Learning
Autoencoding beyond pixels using a learned similarity metric
TLDR
An autoencoder that leverages learned representations to better measure similarities in data space is presented and it is shown that the method learns an embedding in which high-level abstract visual features (e.g. wearing glasses) can be modified using simple arithmetic. Expand
Greedy Layer-Wise Training of Deep Networks
TLDR
These experiments confirm the hypothesis that the greedy layer-wise unsupervised training strategy mostly helps the optimization, by initializing weights in a region near a good local minimum, giving rise to internal distributed representations that are high-level abstractions of the input, bringing better generalization. Expand
Describing Videos by Exploiting Temporal Structure
TLDR
This work proposes an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions and proposes a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Expand
An empirical evaluation of deep architectures on problems with many factors of variation
TLDR
A series of experiments indicate that these models with deep architectures show promise in solving harder learning problems that exhibit many factors of variation. Expand
Meta-Learning for Semi-Supervised Few-Shot Classification
TLDR
This work proposes novel extensions of Prototypical Networks that are augmented with the ability to use unlabeled examples when producing prototypes, and confirms that these models can learn to improve their predictions due to unlabeling examples, much like a semi-supervised algorithm would. Expand
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