Research Area
Year Published

697 Results

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: Mark Tygert, Arthur Szlam, Soumith Chintala, Marc'Aurelio Ranzato, Yuandong Tian, Wojciech Zaremba

June 22, 2015

An Implementation of a Randomized Algorithm for Principal Component Analysis

ArXiv PrePrint

This paper carefully implements newly popular randomized algorithms for principal component analysis and benchmarks them against the classics.

By: Arthur Szlam, Mark Tygert, Yuval Kluger

June 22, 2015

Learning Spatiotemporal Features with 3D Convolutional Networks

ArXiv PrePrint

We propose C3D, a simple and effective approach for spatiotemporal feature using deep 3-dimensional convolutional networks (3D ConvNets) trained on a large scale supervised video dataset.

By: Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, Manohar Paluri

June 22, 2015

Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks

ArXiv PrePrint

We introduce a generative parametric model capable of producing high quality samples of natural images

By: Emily Denton, Soumith Chintala, Arthur Szlam, Rob Fergus

June 22, 2015

Fast Convolutional Nets With fbfft: A GPU Performance Evaluation

International Conference on Learning Representations, 2015

We examine the performance profile of Convolutional Neural Network training on the current generation of NVIDIA Graphics Processing Units.

By: Nicolas Vasilache, Jeff Johnson, Michael Mathieu, Soumith Chintala, Serkan Piantino, Yann LeCun

June 22, 2015

Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets

ArXiv PrePrint

We develop a model that overcomes certain basic limitations of popular deep learning models. We demonstrate its capabilities by learning in an unsupervised way concepts such as simple memorization and binary addition.

By: Armand Joulin, Tomas Mikolov

June 22, 2015

A Theoretical Argument for Complex-Valued Convolutional Networks

ArXiv PrePrint

This article provides foundations for certain convolutional networks.

By: Joan Bruna, Soumith Chintala, Yann LeCun, Serkan Piantino, Arthur Szlam, Mark Tygert

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