September 18, 2016

A MultiPath Network for Object Detection

BMVC

We test three modifications to the standard Fast R-CNN object detector to determine if they can overcome the object detection challenges in a COCO object detection dataset.

Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollar
September 8, 2016

Joint Learning of Speaker and Phonetic Similarities with Siamese Networks

Interspeech 2016

We scale up the feasibility of jointly learning specialized speaker and phone embeddings architectures to the 360 hours of the Librispeech corpus by implementing a sampling method to efficiently select pairs of words from the dataset and improving the loss function.

Neil Zeghidour, Gabriel Synnaeve, Nicolas Usunier, Emmanuel Dupoux
August 16, 2016

Synergy of Monotonic Rules

JMLR

This article describes a method for constructing a special rule (we call it synergy rule) that uses as its input information the outputs (scores) of several monotonic rules which solve the same pattern recognition problem.

Vladimir Vapnik, Rauf Izmailov
August 10, 2016

Neural Network-Based Word Alignment through Score Aggregation

Association for Computational Linguistics Conference on Machine Translation

We present a simple neural network for word alignment that builds source and target word window representations to compute alignment scores for sentence pairs.

Joel Legrand, Michael Auli, Ronan Collobert
June 27, 2016

Unsupervised Learning of Edges

CVPR

Data-driven approaches for edge detection have proven effective and achieve top results on modern benchmarks. However, all current data-driven edge detectors require manual supervision for training in the form of hand-labeled region segments or object boundaries.

Yin Li, Manohar Paluri, James M. Rehg, Piotr Dollar
June 20, 2016

Combining Two and Three-Way Embeddings Models for Link Prediction in Knowledge Bases

Journal of Artificial Intelligence Research, JAIR.org

This paper tackles the problem of endogenous link prediction for Knowledge Base completion.

Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Yves Grandvalet
June 19, 2016

Recurrent Orthogonal Networks and Long-Memory Tasks

International Conference on Machine Learning

This paper analyzes two synthetic datasets originally outlined in (Hochreiter and Schmidhuber, 1997) which are used to evaluate the ability of RNNs to store information over many time steps and explicitly construct RNN solutions to these problems.

Mikael Henaff, Arthur Szlam, Yann LeCun
June 18, 2016

Learning Physical Intuition of Block Towers by Example

International Conference on Machine Learning

Wooden blocks are a common toy for infants, allowing them to develop motor skills and gain intuition about the physical behavior of the world. In this paper, we explore the ability of deep feed-forward models to learn such intuitive physics.

Adam Lerer, Sam Gross, Rob Fergus
June 8, 2016

Key-Value Memory Networks for Directly Reading Documents

EMNLP 2016

This paper introduces a new method, Key-Value Memory Networks, that makes reading documents more viable by utilizing different encodings in the addressing and output stages of the memory read operation.

Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, Jason Weston
May 2, 2016

Deep Multi-Scale Video Prediction Beyond Mean Square Error

ICLR 2016

The paper is about predicting future frames in video sequences given the previous frames.

Michael Mathieu, Camille Couprie, Yann LeCun
May 2, 2016

Predicting Distributions with Linearizing Belief Networks

ICLR 2016: International Conference on Learning Representation

This work introduces a new family of networks called linearizing belief nets or LBNs.

David Grangier
May 2, 2016

Metric Learning with Adaptive Density Discrimination

ICLR

Distance metric learning approaches learn a transformation to a representation space in which distance is in correspondence with a predefined notion of similarity.

Oren Rippel, Manohar Paluri, Piotr Dollar, Lubomir Bourdev
April 19, 2016

Evaluating Prerequisite Qualities for Learning End-to-end Dialog Systems

ICLR 2016

An approach for testing the abilities of conversational agents using question-answering over a knowledge base, personalized recommendations, and natural conversation.

Jesse Dodge, Andreea Gane, Xiang Zhang, Antoine Bordes, Sumit Chopra, Alexander Miller, Arthur Szlam, Jason Weston
April 13, 2016

Abstractive Summarization with Attentive RNN – NAACL 2016

NAACL 2016

Abstractive sentence summarization generates a shorter version of a given sentence while attempting to preserve its meaning. We introduce a conditional recurrent neural network (RNN) which generates a summary of an input sentence.

Sumit Chopra, Michael Auli, Alexander M. Rush
April 1, 2016

The Goldilocks Principle: Reading Children’s Books with Explicit Memory Representations

ICLR 2016

We introduce a new test of how well language models capture meaning in children’s books.

Felix Hill, Antoine Bordes, Sumit Chopra, Jason Weston
January 7, 2016

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

2016 International Conference on Learning Representations

We stabilize Generative Adversarial networks with some architectural constraints and visualize the internals of the networks.

Alec Radford, Luke Metz, Soumith Chintala
December 15, 2015

Learning to Segment Object Candidates

NIPS

In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model obtains substantially higher object recall using fewer proposals. We also show that our model is able to generalize to unseen categories it has not seen during training.

Pedro Oliveira, Ronan Collobert, Piotr Dollar
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.

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.

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

Sainbayar Sukhbaatar, Arthur Szlam, Gabriel Synnaeve, Soumith Chintala, Rob Fergus