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
Facebook AI Research 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 RanzatoJune 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 WestonJune 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 KlugerJune 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 PaluriJune 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 FergusJune 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 LeCunJune 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 MikolovJune 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
Machine Learning 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