February 25, 2017

Automatic Alt-text: Computer-generated Image Descriptions for Blind Users on a Social Network Service


Paper covers the design and deployment of an automatic alt-text (AAT), a system that applies computer vision technology to identify faces, objects, and themes from photos to generate photo alt-text for screen reader users on Facebook.

Shaomei Wu, Jeffrey Wieland, Omid Farivar, Julie Schiller
February 7, 2017

Exploring Normalization in Deep Residual Networks with Concatenated Rectified Linear Units


This paper analyzes the role of Batch Normalization (BatchNorm) layers on ResNets in the hope of improving the current architecture and better incorporating other normalization techniques, such as Normalization Propagation (NormProp), into ResNets.

Wenling Shang, Justin Chiu, Kihyuk Sohn
December 13, 2016

Using Facebook Public Posts to Enhance Trending News Summarization


In this paper we explore using relevant Facebook public posts in addition to the news articles to improve summarization of trending news.

Chen Li, Zhongyu Wei, Yang Liu, Yang Jin, Fei Huang
February 4, 2017

Optimizing Function Placement for Large-Scale Data-Center Applications

International Symposium on Code Generation and Optimization (CGO)

We study the impact of function placement in the context of a simple tool we created that uses sample-based profiling data.

Guilherme Ottoni, Bertrand Maher
January 8, 2017

Optimizing Space Amplification in RocksDB

CIDR 2017

RocksDB is an embedded, high-performance, persistent key-value storage engine developed at Facebook.

Siying Dong, Mark Callaghan, Leonidas Galanis, Dhruba Borthakur, Tony Savor, Michael Stumm
July 14, 2013

TAO: Facebook’s Distributed Data Store for the Social Graph

USENIX Annual Technical Conference 2013

We introduce a simple data model and API tailored for serving the social graph, and TAO, an implementation of this model.

Nathan Bronson, Zach Amsden, George Cabrera, Prasad Chakka, Peter Dimov Hui Ding, Jack Ferris, Anthony Giardullo, Sachin Kulkarni, Harry Li, Mark Marchukov Dmitri Petrov, Lovro Puzar, Yee Jiun Song, Venkat Venkataramani
November 8, 2016

Performance Or Capacity

CMG imPACt, Conference by the Computer Measurement Group

We explore the gap between measurement and aggregation approaches used in performance monitoring, which are not always useful for capacity planning, vs approaches used in capacity planning are often meaningless for performance analysis, and discusses ways to reconcile the two tasks.

Alexander Gilgur, Steve Politis
December 16, 2016

The Paradox of Weak Ties in 55 Countries

Journal of Economic Behavior & Organization

This is the first paper to use a single dataset and methodology to compare the importance of weak ties across countries.

Laura K. Gee, Jason J. Jones, Christopher J. Fariss, Moira Burke, James H. Fowler
December 6, 2016

Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation


We propose a novel neural network architecture to perform weakly-supervised learning by suppressing irrelevant neuron activations. When applied to a practical challenge of transforming satellite images into a map of settlements and individual buildings it delivers results that show superior performance and efficiency.

Xianming Liu, Amy Zhang, Tobias Tiecke, Andreas Gros, Thomas S. Huang
December 6, 2016

Population Density Estimation with Deconvolutional Neural Networks

Workshop on Large Scale Computer Vision at NIPS 2016

This work is part of the Internet.org initiative to provide connectivity all over the world. Population density data is helpful in driving a variety of technology decisions, but currently, a microscopic dataset of population doesn’t exist. Current state of the art population density datasets are at ~1000km2 resolution. To create a better dataset, we have obtained 1PB of satellite imagery at 50cm/pixel resolution to feed through our building classification pipeline.

Amy Zhang, Xianming Liu, Tobias Tiecke, Andreas Gros
December 5, 2016

Semantic Segmentation using Adversarial Networks

Workshop on Adversarial Training at NIPS 2016

Adversarial training has been shown to produce state of the art results for generative image modeling. In this paper we propose an adversarial training approach to train semantic segmentation models.

Pauline Luc, Camille Couprie, Soumith Chintala, Jakob Verbeek
November 2, 2016

DQBarge: Improving Data-Quality Tradeoffs in Large-Scale Internet Services

OSDI 2016

DQBarge is a system that enables better data-quality tradeoffs by propagating critical information along the causal path of request processing.

Jason Flinn, Kaushik Veeraraghavan, Michael Cafarella, Michael Chow
November 2, 2016

Kraken: Leveraging Live Traffic Tests to Identify and Resolve Resource Utilization Bottlenecks in Large Scale Web Services

OSDI 2016

Kraken is a new system that runs load tests by continually shifting live user traffic to one or more data centers.

Kaushik Veeraraghavan, Justin Meza, David Chou, Wonho Kim, Sonia Margulis, Scott Michelson, Rajesh Nishtala, Daniel Obenshain, Dmitri Perelman, Yee Jiun Song
October 31, 2016

Online Social Integration is Associated with Reduced Mortality Risk


We reference 12 million social media profiles against California Department of Public Health vital records and use longitudinal statistical models to assess whether social media use is associated with longer life.

Moira Burke, Will Hobbs, James Fowler, Nicholas Christakis
October 28, 2016

Bilingual Methods for Adaptive Training Data Selection for Machine Translation

Association for the Machine Translation in Americas

We propose a new data selection method which uses semi-supervised convolutional neural networks based on bitokens (Bi-SSCNNs) for training machine translation systems from a large bilingual corpus.us.

Boxing Chen, Roland Kuhn, George Foster, Colin Cherry, Fei Huang
October 10, 2016

Learning to Refine Object Segments

European Conference on Computer Vision

In this work we propose to augment feedforward nets for object segmentation with a novel top-down refinement approach.

Pedro O. Pinheiro, Tsung-Yi Lin, Ronan Collobert, Piotr Dollar
October 10, 2016

Revisiting Visual Question Answering Baselines

European Conference on Computer Vision 2016

This paper questions the value of common practices and develops a simple alternative model based on binary classification.

Allan Jabri, Armand Joulin, Laurens van der Maaten
October 10, 2016

Polysemous Codes

European Conference on Computer Vision 2016 (ECCV)

This paper considers the problem of approximate nearest neighbor search in the compressed domain.

Matthijs Douze, Hervé Jégou, Florent Perronnin
October 8, 2016

Learning Visual Features from Large Weakly Supervised Data

European Conference on Computer Vision

In this paper, we explore the potential of leveraging massive, weakly-labeled image collections for learning good visual features.

Armand Joulin, Laurens van der Maaten, Allan Jabri, Nicolas Vasilache
September 25, 2016

Desugaring Haskell’s do-Notation into Applicative Operations

Haskell Symposium 2016

In this paper we show how to re-use the very same do-notation to work for Applicatives as well, providing efficiency benefits for some types that are both Monad and Applicative, and syntactic convenience for those that are merely Applicative.

Simon Marlow, Simon Peyton Jones, Edward Kmett, Andrey Mokhov