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.

Antoine Bordes, Jason Weston, Nicolas Usunier, Sumit Chopra
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.

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.

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

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.

Nicolas Vasilache, Jeff Johnson, 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.

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.

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

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.

Ning Zhang, Manohar Paluri, Yaniv Taigman, Rob Fergus, Lubomir Bourdev
February 17, 2015

What Makes for Effective Detection Proposals?

PAMI

An in depth study of object proposals and their effect on object detection performance.

Bernt Schiele, Jan Hosang, Piotr Dollar, Rodrigo Benenson
December 19, 2014

Video (language) Modeling: a Baseline for Generative Models of Natural Videos

ArXiv PrePrint

In this work, we investigate models of natural high-resolution video sequences. We show that very simple models borrowed by language modeling applications are surprisingly effective at recovering shor…

Arthur Szlam, Joan Bruna, Marc'Aurelio Ranzato, Michael Mathieu, Ronan Collobert, Sumit Chopra
December 19, 2014

Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews

ICLR workshop 2015

In this work we ensemble several models and achieve state of the art accuracy for predicting the sentiment of movie reviews in the IMDB dataset.

Gregoire Mesnil, Tomas Mikolov, Marc'Aurelio Ranzato, Yoshua Bengio
September 18, 2014

Hierarchical Cascade of Classifiers for Efficient Poselet Evaluation

British Machine Vision Conference

Poselets have been used in a variety of computer vision tasks, such as detection, segmentation, action classification, pose estimation and action recognition, often achieving state-of-the-art performa…

David Bo Chen, Pietro Perona, Lubomir Bourdev
September 4, 2014

Question Answering with Subgraph Embeddings

Empirical Methods in Natural Language Processing

This paper presents a system which learns to answer questions on a broad range of topics from a knowledge base using few handcrafted features. Our model learns low-dimensional embeddings of words and knowledge base constituents; these representations are used to score natural language questions against candidate answers.

Antoine Bordes, Jason Weston, Sumit Chopra
September 4, 2014

#TagSpace: Semantic Embeddings from Hashtags

Empirical Methods in Natural Language Processing

We describe a convolutional neural network that learns feature representations for short textual posts using hashtags as a supervised signal. The proposed approach is trained on up to 5.5 billion words predicting 100,000 possible hashtags.

Jason Weston, Sumit Chopra, Keith Adams
June 24, 2014

DeepFace: Closing the Gap to Human-Level Performance in Face Verification

Conference on Computer Vision and Pattern Recognition (CVPR)

In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing exp…

Yaniv Taigman, Ming Yang, Marc'Aurelio Ranzato, Lior Wolf
June 24, 2014

PANDA: Pose Aligned Networks for Deep Attribute Modeling

Conference on Computer Vision and Pattern Recognition (CVPR)

We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulat…

Ning Zhang, Manohar Paluri, Marc'Aurelio Ranzato, Trevor Darrell, Lubomir Bourdev