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May 31, 2017
Modout: Learning Multi-modal Architectures by Stochastic Regularization
IEEE Conference on Automatic Face and Gesture Recognition (FG 2017)
This paper describes Modout, a model selection method based on stochastic regularization, which is particularly useful in the multi-modal setting.
Fan Li, Natalia Neverova, Christian Wolf, Graham Taylor
April 24, 2017
Multi-Agent Cooperation and the Emergence of (Natural) Language
ICLR 2017
This paper proposes a framework for language learning that relies on multi-agent communication.
Angeliki Lazaridou, Alexander Peysakhovich, Marco BaroniApril 19, 2017
CommAI: Evaluating the first steps towards a useful general AI
ICLR 2017 Workshop
We propose a set of concrete desiderata for general AI, together with a platform to test machines on how well they satisfy such desiderata, while keeping all further complexities to a minimum.
Marco Baroni, Armand Joulin, Allan Jabri, German Kruszewski, Angeliki Lazaridou, Klemen Simonic, Tomas MikolovApril 19, 2017
Joint User-Entity Representation Learning for Event Recommendation in Social Network
2017 IEEE 33rd International Conference on Data Engineering (ICDE)
In this work, we consider the heavy sparseness in both user and event feedback history caused by short lifespans (transiency) of events and user participation patterns in a production event system. We propose to solve the resulting cold-start problems by introducing a joint representation model to project users and events into the same latent space.
Lijun Tang, Eric Yi Liu
March 2, 2017
On user and traffic distribution for extreme rural scenario
Radio Access Network, November 2016
In this contribution, we leverage new research results and data [5] – population density data – that provide a high-resolution view into global population density distributions.
Ali Panah
February 22, 2017
Automatic Alt-text: Computer-generated Image Descriptions for Blind Users on a Social Network Service
CSCW
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.
Jeffrey Wieland, Julie Schiller, Omid Farivar, Shaomei Wu
February 21, 2017
Discussion quality diffuses in the digital public square
WWW 2017
We present the results of a study on large public Facebook pages where we randomly used two different methods—most recent and social feedback—to order comments on posts.
George Berry, Sean TaylorFebruary 7, 2017
Exploring Normalization in Deep Residual Networks with Concatenated Rectified Linear Units
AAAI-17
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 SohnFebruary 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 MaherJanuary 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 StummDecember 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. FowlerDecember 13, 2016
Using Facebook Public Posts to Enhance Trending News Summarization
Coling
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 HuangAcademic Programs
December 6, 2016
Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation
Arxiv
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. HuangDecember 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, Andreas Gros, Tobias Tiecke, Xianming LiuNovember 30, 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 VerbeekNovember 13, 2016
Continuous Deployment of Mobile Software at Facebook (Showcase)
ACM SIGSOFT: International Symposium on the Foundations of Software Engineering (FSE 2016)
This paper describes in detail the software update mobile deployment process at Facebook.
Chuck Rossi, Elisa Shibley, Shi Su, Kent Beck, Tony Savor, Michael StummNovember 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 PolitisNovember 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 ChowNovember 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 SongNovember 1, 2016
Neural Text Generation from Structured Data with Application to the Biography Domain
EMNLP 2016: Conference on Empirical Methods in Natural Language Processing
This paper introduces a neural model for concept-to-text generation that scales to large, rich domains.
Remi Lebret, David Grangier, Michael Auli