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391 Results

September 17, 2017

Passive Realtime Datacenter Fault Detection and Localization

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In this article, we present our passive hybrid approach that combines network path information with end-host-based statistics to rapidly detect and pinpoint the location of datacenter network faults inside a production Facebook datacenter.

By: Arjun Roy, James Hongyi Zeng, Jasmeet Bagga, Alex C. Snoeren
September 7, 2017

Supervised Learning of Universal Sentence Representations from Natural Language Inference Data

Conference on Empirical Methods on Natural Language Processing (EMNLP)

In this paper, we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors (Kiros et al., 2015) on a wide range of transfer tasks.

By: Alexis Conneau, Douwe Kiela, Holger Schwenk, LoÏc Barrault, Antoine Bordes
September 7, 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.

By: 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.

By: Satwik Kottur, José M.F. Moura, Stefan Lee, Dhruv Batra
September 4, 2017

Video Segmentation with Background Motion Models

British Machine Vision Conference (BMVC)

In this paper, we explore the idea of explicitly fitting more general motion models in order to classify trajectories as foreground or background. We find that homographies are sufficient to model a wide variety of background motions found in real-world videos.

By: Scott Wehrwein, Richard Szeliski
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.

By: 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.

By: 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.

By: 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.

By: Louis F. DeKoven, Stefan Savage, Geoffrey M. Voelker, Nektarios Leontiadis
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.

By: Yann Dauphin, Angela Fan, Michael Auli, David Grangier