Facebook

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

The Ethics of Realism in Virtual and Augmented Reality

Abstract: Digital Catapult, London, United Kingdom, 2 Event Lab, Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain, 3 Institute of Neurosciences, University of Barcelona, Barcelona, Spain, 4 Institute of Cognitive Neuroscience, University College London, London, United Kingdom, Magic Leap, Plantation, FL, United States, Dimension – Hammerhead VR, Wimbledon, United Kingdom, BBC, London, United Kingdom, HTC Vive, Slough, United Kingdom, 9 Facebook AR/VR, London, United Kingdom, 10 Jigsaw, New York, NY, United States, 11 Facebook AR/VR, Menlo Park, CA, United States, Nesta, London, United Kingdom

Analyzing Uncertainty in Neural Machine Translation

Abstract: Machine translation is a popular test bed for research in neural sequence-to-sequence models but despite much recent research, there is still a lack of understanding of these models. Practitioners report performance degradation with large beams, the under-estimation of rare words and a lack of diversity in the final translations. Our study relates some of these issues to the inherent uncertainty of the task, due to the existence of multiple valid translations for a single source sentence, and to the extrinsic uncertainty caused by noisy training data. We propose tools and metrics to assess how uncertainty in the data is captured by the model distribution and how it affects search strategies that generate translations. Our results show that search works remarkably well but that models tend to spread too much probability mass over the hypothesis space. Next, we propose tools to assess model calibration and show how to easily fix some shortcomings of current models. As part of this study, we release multiple human reference translations for two popular benchmarks.

Learning Multiagent Communication with Backpropagation

Abstract: Many tasks in AI require the collaboration of multiple agents. Typically, the communication protocol between agents is manually specified and not altered during training. In this paper we explore a simple neural model, called CommNet, that uses continuous communication for fully cooperative tasks. The model consists of multiple agents and the communication between them is learned alongside their policy. We apply this model to a diverse set of tasks, demonstrating the ability of the agents to learn to communicate amongst themselves, yielding improved performance over non-communicative agents and baselines. In some cases, it is possible to interpret the language devised by the agents, revealing simple but effective strategies for solving the task at hand.

Practical Order-Revealing Encryption with Limited Leakage

Abstract: In an order-preserving encryption scheme, the encryption algorithm produces ciphertexts that preserve the order of their plaintexts. Order-preserving encryption schemes have been studied intensely in the last decade, and yet not much is known about the security of these schemes. Very recently, Boneh eti¾?al. Eurocrypti¾?2015 introduced a generalization of order-preserving encryption, called order-revealing encryption, and presented a construction which achieves this notion with best-possible security. Because their construction relies on multilinear maps, it is too impractical for most applications and therefore remains a theoretical result. In this work, we build efficiently implementable order-revealing encryption from pseudorandom functions. We present the first efficient order-revealing encryption scheme which achieves a simulation-based security notion with respect to a leakage function that precisely quantifies what is leaked by the scheme. In fact, ciphertexts in our scheme are only about 1.6 times longer than their plaintexts. Moreover, we show how composing our construction with existing order-preserving encryption schemes results in order-revealing encryption that is strictly more secure than all preceding order-preserving encryption schemes.

Method and apparatus for an application crawler

Abstract: A computer-implemented method is provided for searching for files on the Internet. In one embodiment, the method may provide an application crawler that assembles and dynamically instantiates all components of a web page. The instantiated web application may then be analyzed to locate desired components on the web page. This may involve finding and analyzing all clickable items in the application, driving the web application by injecting events, and extracting information from the application and writing it to a file or database.