September 9, 2017

Grasping the Finer Point: A Supervised Similarity Network for Metaphor Detection

The Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we present the first deep learning architecture designed to capture metaphorical composition. Our results demonstrate that it outperforms the existing approaches in the metaphor identification task.

Marek Rei, Luana Bulat, Douwe Kiela, Ekaterina Shutova
September 7, 2017

Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog

Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, using a Task & Talk reference game between two agents as a testbed, we present a sequence of ‘negative’ results culminating in a ‘positive’ one – showing that while most agent-invented languages are effective (i.e. achieve near-perfect task rewards), they are decidedly not interpretable or compositional.

Satwik Kottur, José M.F. Moura, Stefan Lee, Dhruv Batra
August 28, 2017

Social Hash Partitioner: A Scalable Distributed Hypergraph Partitioner

Very Large Data Bases Conference (VLDB)

We design and implement a distributed algorithm for balanced k-way hypergraph partitioning that minimizes fanout, a fundamental hypergraph quantity also known as the communication volume and (k − 1)-cut metric, by optimizing a novel objective called probabilistic fanout. This choice allows a simple local search heuristic to achieve comparable solution quality to the best existing hypergraph partitioners.

Igor Kabiljo, Brian Karrer, Mayank Pundir, Sergey Pupyrev, Alon Shalita
August 21, 2017

Engineering Egress with Edge Fabric: Steering Oceans of Content to the World

ACM SIGCOMM

This paper presents Edge Fabric, an SDN-based system we built and deployed to tackle the challenges of point presence for Facebook, which serves over two billion users from dozens of points of presence on six continents.

Brandon Schlinker, Ethan Katz-Bassett, Harsha V. Madhyastha, Hyojeong Kim, Italo Cunha, James Hongyi Zeng, James Quinn, Petr Lapukhov, Saif Hasan, Timothy Cui
August 21, 2017

SilkRoad: Making Stateful Layer-4 Load Balancing Fast and Cheap Using Switching ASICs

Association for Computing Machinery's Special Interest Group on Data Communications (SIGCOMM)

In this paper, we show that up to hundreds of software load balancer (SLB) servers can be replaced by a single modern switching ASIC, potentially reducing the cost of load balancing by over two orders of magnitude. Today, large data centers typically employ hundreds or thousands of servers to load-balance incoming traffic over application servers.

Rui Miao, James Hongyi Zeng, Changhoon Kim, Jeongkeun Lee, Minlan Yu
August 14, 2017

Malicious Browser Extensions at Scale: Bridging the Observability Gap between Web Site and Browser

USENIX Workshop on Cyber Security Experimentation and Test

In this paper we describe an approach used at Facebook for dealing with this problem. We present a methodology whereby users exhibiting suspicious online behaviors are scanned (with permission) to identify the set of extensions in their browser, and those extensions are in turn labelled based on the threat indicators they contain.

Louis F. DeKoven, Stefan Savage, Geoffrey M. Voelker, Nektarios Leontiadis
August 6, 2017

Unsupervised Learning by Predicting Noise

International Conference on Machine Learning (ICML)

Convolutional neural networks provide visual features that perform well in many computer vision applications. However, training these networks requires large amounts of supervision; this paper introduces a generic framework to train such networks, end-to-end, with no supervision. We propose to fix a set of target representations, called Noise As Targets (NAT), and to constrain the deep features to align to them.

Piotr Bojanowski, Armand Joulin
August 6, 2017

Wasserstein Generative Adversarial Networks

International Conference on Machine Learning (ICML)

We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can improve the stability of learning, get rid of problems like mode collapse, and provide meaningful learning curves useful for debugging and hyperparameter searches.

Martin Arjovsky, Soumith Chintala, Leon Bottou
August 6, 2017

Language Modeling with Gated Convolutional Networks

International Conference on Machine Learning (ICML)

The pre-dominant approach to language modeling to date is based on recurrent neural networks. Their success on this task is often linked to their ability to capture unbounded context. In this paper we develop a finite context approach through stacked convolutions, which can be more efficient since they allow parallelization over sequential tokens.

Yann Dauphin, Angela Fan, Michael Auli, David Grangier
August 6, 2017

Efficient Softmax Approximation for GPUs

International Conference on Machine Learning (ICML)

We propose an approximate strategy to efficiently train neural network based language models over very large vocabularies.

Edouard Grave, Armand Joulin, Moustapha Cisse, David Grangier, Hervé Jégou
August 6, 2017

Convolutional Sequence to Sequence Learning

International Conference on Machine Learning (ICML)

We introduce an architecture based entirely on convolutional neural networks.

Jonas Gehring, Michael Auli, David Grangier, Denis Yarats, Yann N. Dauphin
July 31, 2017

Focal Surface Displays

SIGGRAPH 2017

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 31, 2017

Enriching Word Vectors with Subword Information

TACL, Association for Computational Linguistics (ACL 2017)

In this paper, we propose a new approach based on the skipgram model, where each word is represented as a bag of character n-grams.

Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov
July 31, 2017

Learning Multilingual Joint Sentence Embeddings with Neural Machine Translation

ACL workshop on Representation Learning for NLP (ACL)

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

Low-Cost 360 Stereo Photography and Video Capture

SIGGRAPH 2017

In this work, we describe a method that takes images from two 360◦ spherical cameras and synthesizes an omni-directional stereo panorama with stereo in all directions. Our proposed method has a lower equipment cost than camera-ring alternatives, can be assembled with currently available off-the-shelf equipment, and is relatively small and light-weight compared to the alternatives.

Kevin Matzen, Michael Cohen, Bryce Evans, Johannes Kopf, Richard Szeliski
July 30, 2017

Reading Wikipedia to Answer Open-Domain Questions

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 (ACL 2017)

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

Jack Hopkins, Douwe Kiela
July 30, 2017

A Convolutional Encoder Model for Neural Machine Translation

Association for Computational Linguistics 2017

The prevalent approach to neural machine translation relies on bi-directional LSTMs to encode the source sentence. We present a faster and simpler architecture based on a succession of convolutional layers.

Jonas Gehring, Michael Auli, David Grangier, Yann Dauphin
July 24, 2017

Untagging on Social Media: Who Untags, What do they Untag, and Why?

Journal: Computers in Human Behavior

Using de-identified, aggregated behavioral data from Facebook and a survey of 802 people, this paper aims to explore untagging by asking whether untagging occurs similarly to other self-presentation behavior and how people view this strategy.

Jeremy Birnholt, Moira Burke, Annie Steele
July 22, 2017

Densely Connected Convolutional Networks

CVPR 2017

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