6.9k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Meta (United Kingdom)GB158 papers
- Meta (Israel)IL62 papers
- Menlo SchoolUS51 papers
- New York UniversityUS49 papers
- Carnegie Mellon UniversityUS33 papers
- University of California, BerkeleyUS31 papers
- The University of Texas at AustinUS28 papers
- Google (United States)US27 papers
- Georgia Institute of TechnologyUS26 papers
- Stanford UniversityUS25 papers
- Cornell UniversityUS24 papers
- University of WashingtonUS20 papers
10 papers · 2 filters
MazeBase: A Sandbox for Learning from Games
Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve +2
This paper introduces MazeBase: an environment for simple 2D games, designed as a sandbox for machine learning approaches to reasoning and planning. Within it, we create 10 simple…
Sequence Level Training with Recurrent Neural Networks
Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli +1
Many natural language processing applications use language models to generate text. These models are typically trained to predict the next word in a sequence, given the previous wo…
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Alec Radford, Luke Metz, Soumith Chintala
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications. Comparatively, unsupervised learning with CNNs has r…
Better Computer Go Player with Neural Network and Long-term Prediction
Yuandong Tian, Yan Zhu
Competing with top human players in the ancient game of Go has been a long-term goal of artificial intelligence. Go's high branching factor makes traditional search techniques inef…
Unsupervised Deep Embedding for Clustering Analysis
Junyuan Xie, Ross Girshick, Ali Farhadi
Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has fo…
Reducing Overfitting in Deep Networks by Decorrelating Representations
Michael Cogswell, Faruk Ahmed, Ross Girshick +2
One major challenge in training Deep Neural Networks is preventing overfitting. Many techniques such as data augmentation and novel regularizers such as Dropout have been proposed…
Predicting distributions with Linearizing Belief Networks
Yann N. Dauphin, David Grangier
Conditional belief networks introduce stochastic binary variables in neural networks. Contrary to a classical neural network, a belief network can predict more than the expected va…
Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, Yann LeCun
Learning to predict future images from a video sequence involves the construction of an internal representation that models the image evolution accurately, and therefore, to some d…
Reinforcement Learning Neural Turing Machines - Revised
Wojciech Zaremba, Ilya Sutskever
The Neural Turing Machine (NTM) is more expressive than all previously considered models because of its external memory. It can be viewed as a broader effort to use abstract extern…
A mathematical motivation for complex-valued convolutional networks
Joan Bruna, Soumith Chintala, Yann LeCun +3
A complex-valued convolutional network (convnet) implements the repeated application of the following composition of three operations, recursively applying the composition to an in…