April 24, 2017

CommAI: Evaluating the First Steps Towards a Useful General AI

ICLR 2017 Workshop

We propose a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum.

Marco Baroni, Armand Joulin, Allan Jabri, Germán Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomas Mikolov
April 24, 2017

Automatic Rule Extraction from Long Short Term Memory Networks

International Conference on Learning Representations (ICLR) 2017

In this paper we consider Long Short Term Memory networks (LSTMs) and demonstrate a new approach for tracking the importance of a given input to the LSTM for a given output.

W. James Murdoch, Arthur Szlam
April 24, 2017

Learning End-to-End Goal-Oriented Dialog

International Conference on Learning Representations (ICLR)

This paper proposes a testbed to break down the strengths and shortcomings of end-to-end dialog systems in goal-oriented applications.

Antoine Bordes, Y-Lan Boureau, Jason Weston
April 24, 2017

Variable Computation in Recurrent Neural Networks

International Conference on Learning Representations (ICLR) 2017

In this paper, we explore a modification to existing recurrent units which allows them to learn to vary the amount of computation they perform at each step, without prior knowledge of the sequence’s time structure.

Yacine Jernite, Edouard Grave, Armand Joulin, Tomas Mikolov
April 24, 2017

Tracking the World State with Recurrent Entity Networks

International Conference on Learning Representations (ICLR) 2017

We introduce a new model, the Recurrent Entity Network (EntNet). It is equipped with a dynamic long-term memory which allows it to maintain and update a representation of the state of the world as it receives new data.

Mikael Henaff, Jason Weston, Arthur Szlam, Antoine Bordes, Yann LeCun
April 24, 2017

Episodic Exploration for Deep Deterministic Policies for StarCraft Micro-Management

International Conference on Learning Representations (ICLR) 2017

We consider scenarios from the real-time strategy game StarCraft as benchmarks for reinforcement learning algorithms.

Gabriel Synnaeve, Zeming Lin, Soumith Chintala
April 24, 2017

Multi-Agent Cooperation and the Emergence of (Natural) Language

International Conference on Learning Representations (ICLR) 2017

This paper proposes a framework for language learning that relies on multi-agent communication.

Angeliki Lazaridou, Alexander Peysakhovich, Marco Baroni
April 24, 2017

Learning through Dialogue Interactions by Asking Questions

International Conference on Learning Representations (ICLR) 2017

In this work, we explore a dialogue agents ability to interact with users by both responding to questions and by asking questions, and importantly to learn from both types of interaction, by designing a simulator and a set of synthetic tasks in the movie domain that allow such interactions between a learner and a teacher.

Jiwei Li, Alexander Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston
April 24, 2017

Dialogue Learning with Human-in-the-Loop

International Conference on Learning Representations (ICLR) 2017

In this paper we explore interacting with a dialogue partner in a reinforcement learning setting where the bot improves its question-answering ability from feedback a teacher gives following its generated responses.

Jiwei Li, Alexander Miller, Sumit Chopra, Marc'Aurelio Ranzato, Jason Weston
April 3, 2017

Bag of Tricks for Efficient Text Classification

European Chapter of the Association for Computational Linguistics (EACL)

This paper explores a simple and efficient baseline for text classification.

Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov
February 20, 2017

Knowledge transfer in SVM and neural networks

Ann Math Artif Intell

The paper considers general machine learning models, where knowledge transfer is positioned as the main method to improve their convergence properties.

Vladimir Vapnik, Rauf Izmailov
February 7, 2017

Exploring Normalization in Deep Residual Networks with Concatenated Rectified Linear Units

AAAI-17

This paper analyzes the role of Batch Normalization (BatchNorm) layers on ResNets in the hope of improving the current architecture and better incorporating other normalization techniques, such as Normalization Propagation (NormProp), into ResNets.

Wenling Shang, Justin Chiu, Kihyuk Sohn
December 6, 2016

Population Density Estimation with Deconvolutional Neural Networks

Workshop on Large Scale Computer Vision at NIPS 2016

This work is part of the Internet.org initiative to provide connectivity all over the world. Population density data is helpful in driving a variety of technology decisions, but currently, a microscopic dataset of population doesn’t exist. Current state of the art population density datasets are at ~1000km2 resolution. To create a better dataset, we have obtained 1PB of satellite imagery at 50cm/pixel resolution to feed through our building classification pipeline.

Amy Zhang, Andreas Gros, Tobias Tiecke, Xianming Liu
December 6, 2016

Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation

Arxiv

We propose a novel neural network architecture to perform weakly-supervised learning by suppressing irrelevant neuron activations. When applied to a practical challenge of transforming satellite images into a map of settlements and individual buildings it delivers results that show superior performance and efficiency.

Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros, Thomas S. Huang
November 30, 2016

Semantic Segmentation using Adversarial Networks

Workshop on Adversarial Training at NIPS 2016

Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models.

Pauline Luc, Camille Couprie, Soumith Chintala, Jakob Verbeek
November 1, 2016

Neural Text Generation from Structured Data with Application to the Biography Domain

Empirical Methods in Natural Language Processing (EMNLP)

This paper introduces a neural model for concept-to-text generation that scales to large, rich domains.

Remi Lebret, David Grangier, Michael Auli
October 10, 2016

Learning to Refine Object Segments

European Conference on Computer Vision

In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach.

Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollar
October 10, 2016

Revisiting Visual Question Answering Baselines

European Conference on Computer Vision 2016

This paper questions the value of common practices and develops a simple alternative model based on binary classification.

Allan Jabri, Armand Joulin, Laurens van der Maaten
October 10, 2016

Polysemous Codes

European Conference on Computer Vision 2016 (ECCV)

This paper considers the problem of approximate nearest neighbor search in the compressed domain.

Matthijs Douze, Hervé Jégou, Florent Perronnin
October 8, 2016

Learning Visual Features from Large Weakly Supervised Data

European Conference on Computer Vision

In this paper, we explore the potential of leveraging massive, weakly-labeled image collections for learning good visual features.

Allan Jabri, Armand Joulin, Laurens van der Maaten, Nicolas Vasilache