Research Area
Year Published

511 Results

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

June 22, 2015

Large-Scale Simple Question Answering with Memory Networks

ArXiv PrePrint

This paper studies the impact of multitask and transfer learning for simple question answering; a setting for which the reasoning required to answer is quite easy, as long as one can retrieve the correct evidence given a question, which can be difficult in large-scale conditions.

By: Antoine Bordes, Nicolas Usunier, Sumit Chopra, Jason Weston

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 12, 2015

Web-Scale Training for Face Identification

The IEEE Conference on Computer Vision and Pattern Recognition

We study face recognition and show that three distinct properties have surprising effects on the transferability of deep convolutional networks (CNN)

By: Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf

June 1, 2015

Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues

International Conference on Computer Vision and Pattern Recognition

We propose a method for person recognition from arbitrary viewpoint and pose.

By: Ning Zhang, Manohar Paluri, Yaniv Taigman, Rob Fergus, Lubomir Bourdev