October 22, 2017

Mask R-CNN

International Conference on Computer Vision (ICCV)

We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition.

Kaiming He, Georgia Gkioxari, Piotr Dollar, Ross Girshick
October 22, 2017

Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training

International Conference on Computer Vision (ICCV)

While strong progress has been made in image captioning recently, machine and human captions are still quite distinct. To address the challenges in this area, we change the training objective of the caption generator from reproducing ground-truth captions to generating a set of captions that is indistinguishable from human written captions.

Rakshith Shetty, Marcus Rohrbach, Lisa Anne Hendricks, Mario Fritz, Bernt Schiele
October 22, 2017

Unsupervised Creation of Parameterized Avatars

International Conference on Computer Vision (ICCV)

We study the problem of mapping an input image to a tied pair consisting of a vector of parameters and an image that is created using a graphical engine from the vector of parameters. The mapping’s objective is to have the output image as similar as possible to the input image.

Lior Wolf, Yaniv Taigman, Adam Polyak
October 22, 2017

Deltille Grids for Geometric Camera Calibration

International Conference on Computer Vision (ICCV)

The recent proliferation of high resolution cameras presents an opportunity to achieve unprecedented levels of precision in visual 3D reconstruction. Yet the camera calibration pipeline, developed decades ago using checkerboards, has remained the de facto standard. In this paper, we ask the question: are checkerboards the optimal pattern for high precision calibration?

Hyowon Ha, Michal Perdoch, Hatem Alismail, In So Kweon, Yaser Sheikh
October 22, 2017

Learning to Reason: End-to-End Module Networks for Visual Question Answering

International Conference on Computer Vision (ICCV)

In this paper, we propose End-to-End Module Networks (N2NMNs), which learn to reason by directly predicting instance-specific network layouts without the aid of a parser.

Ronghang Hu, Jacob Andreas, Marcus Rohrbach, Trevor Darrell, Kate Saenko
October 22, 2017

Segmentation-Aware Convolutional Networks using Local Attention Masks

International Conference on Computer Vision (ICCV)

We introduce an approach to integrate segmentation information within a convolutional neural network (CNN). This counter-acts the tendency of CNNs to smooth information across regions and increases their spatial precision.

Adam W. Harley, Konstantinos G. Derpanis, Iasonas Kokkinos
October 9, 2017

Labeling And Direction Of Slider Questions

International Journal of Market Research

Using a web survey experiment, this study examines measurement comparability between two radio button questions (fully labelled and endpoint labelled) with slider questions unique to web surveys.

Mingnan Liu
October 8, 2017

Living a Discrete Life in a Continuous World: Reference in Cross-Modal Entity Tracking

Proceedings of IWCS (12th International Conference on Computational Semantics)

This paper (a) introduces a concrete referential task to test both aspects, called cross-modal entity tracking; (b) proposes a neural network architecture that uses external memory to build an entity library inspired in the DRSs of DRT, with a mechanism to dynamically introduce new referents or add information to referents that are already in the library.

Gemma Boleda, Sebastian Pado', Nghia The Pham, Marco Baroni
October 5, 2017

STARDATA: a StarCraft AI Research Dataset

Association for the Advancement of Artificial Intelligence Digital Entertainment Conference

We release a dataset of 65646 StarCraft replays that contains 1535 million frames and 496 million player actions. We provide full game state data along with the original replays that can be viewed in StarCraft.

Zeming Lin, Jonas Gehring, Vasil Khalidov, Gabriel Synnaeve
September 24, 2017

Propagation of Joint Space Quantization Error to Operational Space Coordinates and Their Derivatives

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

In this paper, we characterize encoder error in a robotic system. Given encoder specifications, robot kinematics, and discrete transfer functions mapping coordinates to their derivatives, we describe the propagation of quantization error on joint space coordinates to operational space coordinates, joint space coordinate derivatives, and operational space coordinate derivatives.

Nick Colonnese, Allison M. Okamura
September 20, 2017

100Gb/s CWDM4 Optical Interconnect at Facebook Data Centers for Bandwidth Enhancement

Frontiers in Optics / Laser Science (FiO/LS)

Facebook has developed 100G data centers from the ground-up by fine tuning optical technologies, optimizing link-budget, limiting operating temperatures and ultimately improving manufacturability. 100G-CWDM4 is an effective technology to enable connectivity over duplex single-mode fiber.

Abhijit Chakravarty, Katharine Schmidtke, Vincent Zeng, Srinivasan Giridharan, Cathie Deal, Reza Niazmand
September 20, 2017

Characterizing Large-Scale Production Reliability for 100G Optical Interconnect in Facebook Data Centers

Frontiers in Optics / Laser Science (FiO/LS)

Facebook is deploying cost effective 100G CWDM4 transceivers in data centers. This paper describes the post production performance monitoring system which is being implemented to identify optical interconnect early failure modes.

Abhijit Chakravarty, Srinivasan Giridharan, Matt Kelly, Ashwin Poojary, Vincent Zeng
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.

Arjun Roy, James Hongyi Zeng, Jasmeet Bagga, Alex C. Snoeren
September 9, 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.

Alexis Conneau, Douwe Kiela, Holger Schwenk, Loıc Barrault, Antoine Bordes
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