3.7k citations · 11.8k across the 85 of their papers we have counts for
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Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Zhilin Yang, Saizheng Zhang, Jack Urbanek +5
Contrary to most natural language processing research, which makes use of static datasets, humans learn language interactively, grounded in an environment. In this work we propose…
Optimizing the Latent Space of Generative Networks
Piotr Bojanowski, Armand Joulin, David Lopez-Paz +1
Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images. In most successful applications, GAN models share two co…
Low-shot learning with large-scale diffusion
Matthijs Douze, Arthur Szlam, Bharath Hariharan +1
This paper considers the problem of inferring image labels from images when only a few annotated examples are available at training time. This setup is often referred to as low-sho…
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…
Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play
Sainbayar Sukhbaatar, Zeming Lin, Ilya Kostrikov +3
We describe a simple scheme that allows an agent to learn about its environment in an unsupervised manner. Our scheme pits two versions of the same agent, Alice and Bob, against on…
Automatic Rule Extraction from Long Short Term Memory Networks
W. James Murdoch, Arthur Szlam
Although deep learning models have proven effective at solving problems in natural language processing, the mechanism by which they come to their conclusions is often unclear. As a…
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…