June 18, 2016

Learning Physical Intuition of Block Towers by Example

International Conference on Machine Learning

Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world. In this paper, we explore the ability of deep feed-forward models to learn such intuitive physics.

Adam Lerer, Sam Gross, Rob Fergus
June 8, 2016

Key-Value Memory Networks for Directly Reading Documents

EMNLP 2016

This paper introduces a new method, Key-Value Memory Networks, that makes reading documents more viable by utilizing different encodings in the addressing and output stages of the memory read operation.

Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, Jason Weston
May 2, 2016

Deep Multi-Scale Video Prediction Beyond Mean Square Error

ICLR 2016

The paper is about predicting future frames in video sequences given the previous frames.

Michael Mathieu, Camille Couprie, Yann LeCun
May 2, 2016

Predicting Distributions with Linearizing Belief Networks

ICLR 2016: International Conference on Learning Representation

This work introduces a new family of networks called linearizing belief nets or LBNs.

David Grangier
May 2, 2016

Metric Learning with Adaptive Density Discrimination


Distance metric learning approaches learn a transformation to a representation space in which distance is in correspondence with a predefined notion of similarity.

Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev
April 19, 2016

Evaluating Prerequisite Qualities for Learning End-to-end Dialog Systems

ICLR 2016

An approach for testing the abilities of conversational agents using question-answering over a knowledge base, personalized recommendations, and natural conversation.

Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander Miller, Arthur Szlam, Jason Weston
April 13, 2016

Abstractive Summarization with Attentive RNN – NAACL 2016

NAACL 2016

Abstractive sentence summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence.

Sumit Chopra, Michael Auli, Alexander M. Rush
April 1, 2016

The Goldilocks Principle: Reading Children’s Books with Explicit Memory Representations

ICLR 2016

We introduce a new test of how well language models capture meaning in children’s books.

Felix Hill, Antoine Bordes, Sumit Chopra, Jason Weston
January 7, 2016

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

2016 International Conference on Learning Representations

We stabilize Generative Adversarial networks with some architectural constraints and visualize the internals of the networks.

Alec Radford, Luke Metz, Soumith Chintala
December 15, 2015

Learning to Segment Object Candidates


In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model obtains substantially higher object recall using fewer proposals. We also show that our model is able to generalize to unseen categories it has not seen during training.

Pedro Oliveira, Ronan Collobert, Piotr Dollar
December 7, 2015

Simple Bag-of-Words Baseline for Visual Question Answering

ArXiv PrePrint

We describe a very simple bag-of-words baseline for visual question answering.

Arthur Szlam, Bolei Zhou, Rob Fergus, Sainbayar Sukhbaatar, Yuandong Tian
November 25, 2015

A Roadmap Towards Machine Intelligence

ArXiv PrePrint

We describe one possible roadmap how to develop intelligent machines with communication skills that can perform useful tasks for us.

Armand Joulin, Marco Baroni, Tomas Mikolov
November 23, 2015

MazeBase: A Sandbox for Learning from Games

ArXiv PrePrint

Environment for simple 2D maze games, designed as a sandbox for machine learning approaches to reasoning and planning

Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve, Soumith Chintala, Rob Fergus
November 23, 2015

Learning Simple Algorithms from Examples

ArXiv PrePrint

We present an approach for learning simple algorithms such as addition or multiplication. Our methods works as a hard attention model on both input and output and it is learn with reinforcement learning.

Wojciech Zaremba, Armand Joulin, Tomas Mikolov, Rob Fergus
November 20, 2015

Sequence Level Training with Recurrent Neural Networks

ICLR 2016

This work aims at improving text generation for applications such as summarization, machine translation and image captioning. The key idea is to learn to predict not just the next word but a whole sequence of words, and to train end-to-end at the sequence level.

Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba
November 19, 2015

Alternative Structures for Character-Level RNNs

ArXiv PrePrint

We present two alternative structures to character level recurrent networks. Character level RNNs are known to be very inefficient while achieving lower performance than word level RNNs.

Piotr Bojanowski, Tomas Mikolov, Armand Joulin
August 12, 2015

No Regret Bound for Extreme Bandits

ArXiv PrePrint

A sensible notion of regret in the extreme bandit setting

Robert Nishihara, David Lopez-Paz, Leon Bottou
June 25, 2015

Scale-Invariant Learning and Convolutional Networks

ArXiv PrePrint

The conventional classification schemes — notably multinomial logistic regression — used in conjunction with convolutional networks (convnets) are classical in statistics, designed without consideration for the usual coupling with convnets, stochastic gradient descent, and backpropagation. In the specific application to supervised learning for convnets, a simple scale-invariant classification stage turns out to be more robust than multinomial logistic regression, appears to result in slightly lower errors on several standard test sets, has similar computational costs, and features precise control over the actual rate of learning.

Arthur Szlam, Marc'Aurelio Ranzato, Mark Tygert, Soumith Chintala, Wojciech Zaremba, Yuandong Tian
June 22, 2015

End-To-End Memory Networks

NIPS 2015

End-to-end training of Memory Networks on question answering and to language modeling

Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus
June 22, 2015

Learning Longer Memory in Recurrent Neural Networks

Workshop at ICLR 2015

We describe simple extension of recurrent networks that allows them to learn longer term memory. Usefulness is demonstrated in improved performance on standard language modeling tasks.

Tomas Mikolov, Armand Joulin, Sumit Chopra, Michael Mathieu, Marc'Aurelio Ranzato