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Research Areas
Year Published

62 Results

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.

By: 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

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.

By: 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.

By: 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.

By: 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.

By: 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.

By: 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.

By: 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

By: 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.

By: 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.

By: Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, Wojciech Zaremba