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129 Results

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
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.

By: 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

By: 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.

By: 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

By: 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.

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