933 citations · 6k across the 51 of their papers we have counts for
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Classical Structured Prediction Losses for Sequence to Sequence Learning
Sergey Edunov, Myle Ott, Michael Auli +2
There has been much recent work on training neural attention models at the sequence-level using either reinforcement learning-style methods or by optimizing the beam. In this paper…
Unsupervised Machine Translation Using Monolingual Corpora Only
Guillaume Lample, Alexis Conneau, Ludovic Denoyer +1
Machine translation has recently achieved impressive performance thanks to recent advances in deep learning and the availability of large-scale parallel corpora. There have been nu…
Word Translation Without Parallel Data
Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato +2
State-of-the-art methods for learning cross-lingual word embeddings have relied on bilingual dictionaries or parallel corpora. Recent studies showed that the need for parallel data…
Gradient Episodic Memory for Continual Learning
David Lopez-Paz, Marc'Aurelio Ranzato
One major obstacle towards AI is the poor ability of models to solve new problems quicker, and without forgetting previously acquired knowledge. To better understand this issue, we…
Fader Networks: Manipulating Images by Sliding Attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier +3
This paper introduces a new encoder-decoder architecture that is trained to reconstruct images by disentangling the salient information of the image and the values of attributes di…
Hard Mixtures of Experts for Large Scale Weakly Supervised Vision
Sam Gross, Marc'Aurelio Ranzato, Arthur Szlam
Training convolutional networks (CNN's) that fit on a single GPU with minibatch stochastic gradient descent has become effective in practice. However, there is still no effective m…
Training Language Models Using Target-Propagation
Sam Wiseman, Sumit Chopra, Marc'Aurelio Ranzato +4
While Truncated Back-Propagation through Time (BPTT) is the most popular approach to training Recurrent Neural Networks (RNNs), it suffers from being inherently sequential (making…
Transformation-Based Models of Video Sequences
Joost van Amersfoort, Anitha Kannan, Marc'Aurelio Ranzato +3
In this work we propose a simple unsupervised approach for next frame prediction in video. Instead of directly predicting the pixels in a frame given past frames, we predict the tr…