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 31, 2017

Focal Surface Displays


We introduce focal surface displays to meet the challenge of vergence-accommodation conflict, augmenting conventional HMDs with a phase-only spatial light modulator (SLM) placed between the display screen and viewing optics. This SLM acts as a dynamic freeform lens, shaping synthesized focal surfaces to conform to the virtual scene geometry.

Nathan Matsuda, Alexander Fix, Douglas Lanman
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


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 15, 2017

Formulation of Aerial Platform Environmental Requirements from Global Radiosonde Data

Journal of Unmanned Vehicle Systems

This paper demonstrates how raw observational climate data can be processed to characterize global climate histories and statistics for the formulation of aerial platform environmental requirements.

Jacob Stelman, Zhang Liu
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
June 6, 2017

Analysis of Effective Impedance Transmitted to the Operator in Position-Exchange Bilateral Teleoperation

IEEE World Haptics 2017

In this paper, we analyze the impedance transmitted to the operator in bilateral teleoperation including the effects of master and slave dynamics, local and communication time delay, low-pass filtering of the velocity estimate, and controller stiffness and damping, for three different environment dynamics: free, clamped, and a mass-damper-spring.

Nick Colonnese, Allison M. Okamura
May 22, 2017

IVD: Automatic Learning and Enforcement of Authorization Rules in Online Social Networks

IEEE Symposium on Security and Privacy (IEEE S&P)

In this paper, we propose Invariant Detector (IVD), a defense-in-depth system that automatically learns authorization rules from normal data manipulation patterns and distills them into likely invariants.

Paul Marinescu, Chad Parry, Marjori Pomarole, Yuan Tian, Patrick Tague, Ioannis Papagiannis
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 27, 2017

Passive Realtime Datacenter Fault Detection

USENIX Symposium on Networked Systems Design and Implementation (NSDI) 2017

We describe how to expedite the process of detecting and localizing partial datacenter faults using an end-host method generalizable to most datacenter applications.

Arjun Roy, James Hongyi Zeng, Jasmeet Bagga, Alex C. Snoeren
April 24, 2017

Connective recovery in social networks after the death of a friend

Nature Human Behavior

Most individuals have few close friends, leading to potential isolation after a friend’s death. Do social networks heal to fill the space left by the loss? We conduct such a study of self-healing and resilience in social networks.

William Hobbs, Moira Burke
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