All Research Areas
Research Areas
Year Published

388 Results

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.

By: Chen Li, Zhongyu Wei, Yang Liu, Yang Jin, Fei Huang
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.

By: 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 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.

By: Amy Zhang, Andreas Gros, Tobias Tiecke, Xianming Liu
November 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.

By: Pauline Luc, Camille Couprie, Soumith Chintala, Jakob Verbeek
November 16, 2016

Performance or Capacity? Different Approaches for Different Tasks

International Conference for Performance and Capacity (CMGimPACt)

Measurement and aggregation approaches that are used in performance monitoring are not always useful for capacity planning, while approaches that we use in capacity planning are often meaningless for performance analysis. This paper explores this gap and discusses ways to reconcile the two tasks.

By: Alexander Gilgur, Steve Politis
November 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.

By: Chuck Rossi, Elisa Shibley, Shi Su, Kent Beck, Tony Savor, Michael Stumm
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.

By: Alexander Gilgur, Steve Politis
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.

By: 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.

By: Kaushik Veeraraghavan, Justin Meza, David Chou, Wonho Kim, Sonia Margulis, Scott Michelson, Rajesh Nishtala, Daniel Obenshain, Dmitri Perelman, Yee Jiun Song
November 1, 2016

Neural Text Generation from Structured Data with Application to the Biography Domain

Empirical Methods in Natural Language Processing (EMNLP)

This paper introduces a neural model for concept-to-text generation that scales to large, rich domains.

By: Remi Lebret, David Grangier, Michael Auli