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.

Training Agent for First-Person Shooter Game with Actor-Critic Curriculum Learning

Abstract: In this paper, we propose a novel framework for training vision-based agent for First-Person Shooter (FPS) Game, in particular Doom. Our framework combines the state-of-the-art reinforcement learning approach (Asynchronous Advantage Actor-Critic (A3C) model) with curriculum learning. Our model is simple in design and only uses game states from the AI side, rather than using opponents' information. On a known map, our agent won 10 out of the 11 attended games and the champion of Track1 in ViZDoom AI Competition 2016 by a large margin, 35\% higher score than the second place.

Mining Social Emotions from Affective Text

Abstract: This paper is concerned with the problem of mining social emotions from text. Recently, with the fast development of web 2.0, more and more documents are assigned by social users with emotion labels such as happiness, sadness, and surprise. Such emotions can provide a new aspect for document categorization, and therefore help online users to select related documents based on their emotional preferences. Useful as it is, the ratio with manual emotion labels is still very tiny comparing to the huge amount of web/enterprise documents. In this paper, we aim to discover the connections between social emotions and affective terms and based on which predict the social emotion from text content automatically. More specifically, we propose a joint emotion-topic model by augmenting Latent Dirichlet Allocation with an additional layer for emotion modeling. It first generates a set of latent topics from emotions, followed by generating affective terms from each topic. Experimental results on an online news collection show that the proposed model can effectively identify meaningful latent topics for each emotion. Evaluation on emotion prediction further verifies the effectiveness of the proposed model.

System for instant messaging the sender and recipients of an e-mail message

Abstract: Systems and techniques for transferring electronic data between users of a communications system include a host system structured and arranged to receive and deliver messages of various types between users of the communications system. The host system includes an instant messaging network; a mail gateway; and a configuring network in communication with both the instant messaging network and the mail gateway. The instant messaging network enables instant messaging communication between users of the communications system and has the capability to monitor whether a certain user is capable of receiving an instant message at a particular moment. The mail gateway receives and delivers e-mail messages to users of the communications system. The configuring network is dedicated to automatically configuring instant messaging communication between an intended recipient of an e-mail message and the sender of the e-mail message.

Characterizing, Modeling, and Benchmarking RocksDB Key-Value Workloads at Facebook

Abstract: Persistent key-value stores are widely used as building blocks in today’s IT infrastructure for managing and storing large amounts of data. However, studies of characterizing real-world workloads for key-value stores are limited due to the lack of tracing/analyzing tools and the difficulty of collecting traces in operational environments. In this paper, we first present a detailed characterization of workloads from three typical RocksDB production use cases at Facebook: UDB (a MySQL storage layer for social graph data), ZippyDB (a distributed key-value store), and UP2X (a distributed key-value store for AI/ML services). These characterizations reveal several interesting findings: first, that the distribution of key and value sizes are highly related to the use cases/applications; second, that the accesses to key-value pairs have a good locality and follow certain special patterns; and third, that the collected performance metrics show a strong diurnal pattern in the UDB, but not the other two. We further discover that although the widely used key-value benchmark YCSB provides various workload configurations and key-value pair access distribution models, the YCSBtriggered workloads for underlying storage systems are still not close enough to the workloads we collected due to ignorance of key-space localities. To address this issue, we propose a key-range based modeling and develop a benchmark that can better emulate the workloads of real-world key-value stores. This benchmark can synthetically generate more precise key-value queries that represent the reads and writes of key-value stores to the underlying storage system.

Space-time Neural Irradiance Fields for Free-Viewpoint Video

Abstract: We present a method that learns a spatiotemporal neural irradiance field for dynamic scenes from a single video. Our learned representation enables free-viewpoint rendering of the input video. Our method builds upon recent advances in implicit representations. Learning a spatiotemporal irradiance field from a single video poses significant challenges because the video contains only one observation of the scene at any point in time. The 3D geometry of a scene can be legitimately represented in numerous ways since varying geometry (motion) can be explained with varying appearance and vice versa. We address this ambiguity by constraining the time-varying geometry of our dynamic scene representation using the scene depth estimated from video depth estimation methods, aggregating contents from individual frames into a single global representation. We provide an extensive quantitative evaluation and demonstrate compelling free-viewpoint rendering results.