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Institution

Facebook

CompanyTel Aviv, Israel
About: Facebook is a company organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Artificial neural network & Language model. The organization has 7856 authors who have published 10906 publications receiving 570123 citations. The organization is also known as: facebook.com & FB.


Papers
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Proceedings Article
01 Jan 2017
TL;DR: This work introduces a novel flexible approach named Houdini for generating adversarial examples specifically tailored for the final performance measure of the task considered, be it combinatorial and non-decomposable.
Abstract: Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines. So far, most existing methods only work for classification and are not designed to alter the true performance measure of the problem at hand. We introduce a novel flexible approach named Houdini for generating adversarial examples specifically tailored for the final performance measure of the task considered, be it combinatorial and non-decomposable. We successfully apply Houdini to a range of applications such as speech recognition, pose estimation and semantic segmentation. In all cases, the attacks based on Houdini achieve higher success rate than those based on the traditional surrogates used to train the models while using a less perceptible adversarial perturbation.

107 citations

Proceedings ArticleDOI
Huan-Kai Peng1, Jiang Zhu1, Dongzhen Piao1, Rong Yan2, Ying Zhang1 
11 Dec 2011
TL;DR: This paper proposes modeling the retweet patterns using conditional random fields with a three types of user-tweet features: content influence, network influence and temporal decay factor, and demonstrates that CRF can improve prediction effectiveness by incorporating social relationships compared to the baselines that do not.
Abstract: Among the most popular micro-blogging service, Twitter recently introduced their reblogging service called retweet to allow a user to repopulate another user's content for his followers. It quickly becomes one of the most prominent features on Twitter and an important mean for secondary content promotion. However, it remains unclear what motivates users to retweet and whether the retweeting decisions are predictable based on a user's tweeting history and social relationships. In this paper, we propose modeling the retweet patterns using conditional random fields with a three types of user-tweet features: content influence, network influence and temporal decay factor. We also investigate approaches to partition the social graphs and construct the network relations for retweet prediction. Our experiments demonstrate that CRF can improve prediction effectiveness by incorporating social relationships compared to the baselines that do not.

107 citations

Proceedings ArticleDOI
27 Apr 2013
TL;DR: Using Latent Dirichlet Allocation (LDA), this work identifies topics from more than half a million Facebook status updates and determines which topics are more likely to receive feedback, such as likes and comments.
Abstract: Although both men and women communicate frequently on Facebook, we know little about what they talk about, whether their topics differ and how their network responds. Using Latent Dirichlet Allocation (LDA), we identify topics from more than half a million Facebook status updates and determine which topics are more likely to receive feedback, such as likes and comments. Women tend to share more personal topics (e.g., family matters), while men discuss more public ones (e.g., politics and sports). Generally, women receive more feedback than men, but "male" topics (those more often posted by men) receive more feedback, especially when posted by women.

107 citations

Posted Content
TL;DR: This paper gives a formalization of a shared pattern of approximating the solution to a nested optimization problem, which it is called GIMLI, proves its general requirements, and derives a general-purpose algorithm for implementing similar approaches.
Abstract: Many (but not all) approaches self-qualifying as "meta-learning" in deep learning and reinforcement learning fit a common pattern of approximating the solution to a nested optimization problem. In this paper, we give a formalization of this shared pattern, which we call GIMLI, prove its general requirements, and derive a general-purpose algorithm for implementing similar approaches. Based on this analysis and algorithm, we describe a library of our design, higher, which we share with the community to assist and enable future research into these kinds of meta-learning approaches. We end the paper by showcasing the practical applications of this framework and library through illustrative experiments and ablation studies which they facilitate.

107 citations

Proceedings ArticleDOI
04 Oct 2015
TL;DR: This paper gives a comprehensive description of the use cases, design, implementation, and usage statistics of a suite of tools that manage Facebook's configuration end-to-end, including the frontend products, backend systems, and mobile apps.
Abstract: Facebook's web site and mobile apps are very dynamic. Every day, they undergo thousands of online configuration changes, and execute trillions of configuration checks to personalize the product features experienced by hundreds of million of daily active users. For example, configuration changes help manage the rollouts of new product features, perform A/B testing experiments on mobile devices to identify the best echo-canceling parameters for VoIP, rebalance the load across global regions, and deploy the latest machine learning models to improve News Feed ranking. This paper gives a comprehensive description of the use cases, design, implementation, and usage statistics of a suite of tools that manage Facebook's configuration end-to-end, including the frontend products, backend systems, and mobile apps.

107 citations


Authors

Showing all 7875 results

NameH-indexPapersCitations
Yoshua Bengio2021033420313
Xiang Zhang1541733117576
Jitendra Malik151493165087
Trevor Darrell148678181113
Christopher D. Manning138499147595
Robert W. Heath128104973171
Pieter Abbeel12658970911
Yann LeCun121369171211
Li Fei-Fei120420145574
Jon Kleinberg11744487865
Sergey Levine11565259769
Richard Szeliski11335972019
Sanjeev Kumar113132554386
Bruce Neal10856187213
Larry S. Davis10769349714
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202237
20211,738
20202,017
20191,607
20181,229