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.

Learning to Separate Object Sounds by Watching Unlabeled Video

Abstract: Perceiving a scene most fully requires all the senses. Yet modeling how objects look and sound is challenging: most natural scenes and events contain multiple objects, and the audio track mixes all the sound sources together. We propose to learn audio-visual object models from unlabeled video, then exploit the visual context to perform audio source separation in novel videos. Our approach relies on a deep multi-instance multi-label learning framework to disentangle the audio frequency bases that map to individual visual objects, even without observing/hearing those objects in isolation. We show how the recovered disentangled bases can be used to guide audio source separation to obtain better-separated, object-level sounds. Our work is the first to learn audio source separation from large-scale “in the wild” videos containing multiple audio sources per video. We obtain state-of-the-art results on visually-aided audio source separation and audio denoising. Our video results: http://vision.cs.utexas.edu/projects/separating_object_sounds/.

The Architectural Implications of Facebook's DNN-Based Personalized Recommendation

Abstract: The widespread application of deep learning has changed the landscape of computation in data centers. In particular, personalized recommendation for content ranking is now largely accomplished using deep neural networks. However, despite their importance and the amount of compute cycles they consume, relatively little research attention has been devoted to recommendation systems. To facilitate research and advance the understanding of these workloads, this paper presents a set of real-world, production-scale DNNs for personalized recommendation coupled with relevant performance metrics for evaluation. In addition to releasing a set of open-source workloads, we conduct in-depth analysis that underpins future system design and optimization for at-scale recommendation: Inference latency varies by 60% across three Intel server generations, batching and co-location of inference jobs can drastically improve latency-bounded throughput, and diversity across recommendation models leads to different optimization strategies.

A "hitchhiker's" guide to fast and efficient data reconstruction in erasure-coded data centers

Abstract: Erasure codes such as Reed-Solomon (RS) codes are being extensively deployed in data centers since they offer significantly higher reliability than data replication methods at much lower storage overheads. These codes however mandate much higher resources with respect to network bandwidth and disk IO during reconstruction of data that is missing or otherwise unavailable. Existing solutions to this problem either demand additional storage space or severely limit the choice of the system parameters. In this paper, we present "Hitchhiker", a new erasure-coded storage system that reduces both network traffic and disk IO by around 25% to 45% during reconstruction of missing or otherwise unavailable data, with no additional storage, the same fault tolerance, and arbitrary flexibility in the choice of parameters, as compared to RS-based systems. Hitchhiker 'rides' on top of RS codes, and is based on novel encoding and decoding techniques that will be presented in this paper. We have implemented Hitchhiker in the Hadoop Distributed File System (HDFS). When evaluating various metrics on the data-warehouse cluster in production at Facebook with real-time traffic and workloads, during reconstruction, we observe a 36% reduction in the computation time and a 32% reduction in the data read time, in addition to the 35% reduction in network traffic and disk IO. Hitchhiker can thus reduce the latency of degraded reads and perform faster recovery from failed or decommissioned machines.

Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs

Abstract: RDF question/answering (Q/A) allows users to ask questions in natural languages over a knowledge base represented by RDF. To answer a natural language question, the existing work takes a two-stage approach: question understanding and query evaluation. Their focus is on question understanding to deal with the disambiguation of the natural language phrases. The most common technique is the joint disambiguation, which has the exponential search space. In this paper, we propose a systematic framework to answer natural language questions over RDF repository (RDF Q/A) from a graph data-driven perspective. We propose a semantic query graph to model the query intention in the natural language question in a structural way, based on which, RDF Q/A is reduced to subgraph matching problem. More importantly, we resolve the ambiguity of natural language questions at the time when matches of query are found. The cost of disambiguation is saved if there are no matching found. More specifically, we propose two different frameworks to build the semantic query graph, one is relation (edge)-first and the other one is node-first. We compare our method with some state-of-the-art RDF Q/A systems in the benchmark dataset. Extensive experiments confirm that our method not only improves the precision but also speeds up query performance greatly.

Location ranking using social graph information

Abstract: In one embodiment, a user of a social networking system requests to check in a place near the user's current location. The social networking system generates a list of places near the user's current location, ranks the places in the list of places near the user's current location by a distance between each place and the user's current location, as well as activity of the user and the user's social contacts for each place, and returns the ranked list to the user.