August 3, 2017

Learning Multilingual Joint Sentence Embeddings with Neural Machine Translation

ACL workshop on Representation Learning for NLP 2017

In this paper, we use the framework of neural machine translation to learn joint sentence representations across six very different languages. Our aim is that a representation which is independent of the language, is likely to capture the underlying semantics.

Holger Schwenk, Matthijs Douze
July 30, 2017

Reading Wikipedia to Answer Open-Domain Questions

Annual Meeting of the Association for Computational Linguistics (ACL 2017)

This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article.

Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes
July 30, 2017

Automatically Generating Rhythmic Verse with Neural Networks

Association for Computational Linguistics 2017

We propose two novel methodologies for the automatic generation of rhythmic poetry in a variety of forms.

Jack Hopkins, Douwe Kiela
July 21, 2017

Learning Features by Watching Objects Move

CVPR 2017

This paper presents a novel yet intuitive approach to unsupervised feature learning. Inspired by the human visual system, we explore whether low-level motion-based grouping cues can be used to learn an effective visual representation.

Deepak Pathak, Ross Girshick, Piotr Dollar, Trevor Darrell, Bharath Hariharan
July 21, 2017

Feature Pyramid Networks for Object Detection

CVPR 2017

In this paper, we exploit the inherent multi-scale, pyramidal hierarchy of deep convolutional networks to construct feature pyramids with marginal extra cost.

Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie
July 21, 2017

Semantic Amodal Segmentation

CVPR

Common visual recognition tasks such as classification, object detection, and semantic segmentation are rapidly reaching maturity, and given the recent rate of progress, it is not unreasonable to conjecture that techniques for many of these problems will approach human levels of performance in the next few years. In this paper we look to the future: what is the next frontier in visual recognition?

Yan Zhu, Yuandong Tian, Dimitris Mexatas, Piotr Dollar
July 21, 2017

Aggregated Residual Transformations for Deep Neural Networks

CVPR 2017

We present a simple, highly modularized network architecture for image classification.

Saining Xie, Ross Girshick, Piotr Dollar, Zhuowen Tu, Kaiming He
June 8, 2017

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Data @ Scale

In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization.

Priya Goyal, Piotr Dollar, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He
May 10, 2017

Densely Connected Convolutional Networks

IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

In this paper, we embrace the observation that hat convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output, and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion.

Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger
April 24, 2017

Towards Principled Methods for Training Generative Adversarial Networks

International Conference on Learning Representations (ICLR) 2017

The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. I

Martin Arjovsky, Leon Bottou
April 24, 2017

Revisiting Classifier Two-Sample Tests for GAN Evaluation and Causal Discovery

International Conference on Learning Representations (ICLR) 2017

In this paper, we aim to revive interest in the use of binary classifiers for two-sample testing. To this end, we review their fundamentals, previous literature on their use, compare their performance against alternative state-of-the-art two-sample tests, and propose them to evaluate generative adversarial network models applied to image synthesis.

David Lopez-Paz, Maxime Oquab
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

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

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.

Nicolas Usunier∗, Gabriel Synnaeve∗, Zeming Lin, Soumith Chintala
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 H. 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 H. Miller, Sumit Chopra, Marc’Aurelio Ranzato, Jason Weston
April 24, 2017

Training Agent for First-Person Shooter Game With Actor-Critic Curriculum Learning

International Conference on Learning Representations (ICLR) 2017

In this paper, we propose a new framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom.

Yuxin Wu, Yuandong Tian
April 24, 2017

Unsupervised Cross-Domain Image Generation

International Conference on Learning Representations (ICLR) 2017

We study the problem of transferring a sample in one domain to an analog sample in another domain. Given two related domains, S and T, we would like to learn a generative function G that maps an input sample from S to the domain T, such that the output of a given representation function f, which accepts inputs in either domains, would remain unchanged.

Yaniv Taigman, Adam Polyak, Lior Wolf
April 24, 2017

An Analytical Formula of Population Gradient for Two-Layered ReLU network and its Applications in Convergence and Critical Point Analysis

International Conference on Learning Representations (ICLR) 2017

In this paper, we explore theoretical properties of training a two-layered ReLU network g(x; w) = PK j=1 σ(w | j x) with centered d-dimensional spherical Gaussian input x (σ=ReLU). We train our network with gradient descent on w to mimic the output of a teacher network with the same architecture and fixed parameters w∗.

Yuandong Tian
April 24, 2017

Improving Neural Language Models with a Continuous Cache

International Conference on Learning Representations (ICLR) 2017

We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is […]

Edouard Grave, Armand Joulin, Nicolas Usunier