Facebook

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.

Providing context relevant search for a user based on location and social information

Abstract: A social networking system provides relevant third-party content objects to users by matching user location, interests, and other social information with the content, location, and timing associated with the content objects. Content objects are provided based on relevance scores specific to a user. Relevance scores may be calculated based on the user's previous interactions with content object notifications, or based on interests that are common between the user and his or her connections in the social network. Context search is also provided for a user, wherein a list of search of results is ranked according to the relevance score of content object associated with the search results. Notifications may also be priced and distributed to users based on their relevance. In this way, the system can provide notifications that are relevant to user's interests and current circumstances, increasing the likelihood that they will find content objects of interest.

Memory Networks

Abstract: We describe a new class of learning models called memory networks Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly The long-term memory can be read and written to, with the goal of using it for prediction We investigate these models in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base, and the output is a textual response We evaluate them on a large-scale QA task, and a smaller, but more complex, toy task generated from a simulated world In the latter, we show the reasoning power of such models by chaining multiple supporting sentences to answer questions that require understanding the intension of verbs

Real Time Speech Enhancement in the Waveform Domain.

Abstract: We present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises, as well as room reverb. Additionally, we suggest a set of data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. We perform evaluations on several standard benchmarks, both using objective metrics and human judgements. The proposed model matches state-of-the-art performance of both causal and non causal methods while working directly on the raw waveform.

The mystery machine: end-to-end performance analysis of large-scale internet services

Abstract: Current debugging and optimization methods scale poorly to deal with the complexity of modern Internet services, in which a single request triggers parallel execution of numerous heterogeneous software components over a distributed set of computers The Achilles' heel of current methods is the need for a complete and accurate model of the system under observation: producing such a model is challenging because it requires either assimilating the collective knowledge of hundreds of programmers responsible for the individual components or restricting the ways in which components interactFortunately, the scale of modern Internet services offers a compensating benefit: the sheer volume of requests serviced means that, even at low sampling rates, one can gather a tremendous amount of empirical performance observations and apply "big data" techniques to analyze those observations In this paper, we show how one can automatically construct a model of request execution from pre-existing component logs by generating a large number of potential hypotheses about program behavior and rejecting hypotheses contradicted by the empirical observations We also show how one can validate potential performance improvements without costly implementation effort by leveraging the variation in component behavior that arises naturally over large numbers of requests to measure the impact of optimizing individual components or changing scheduling behaviorWe validate our methodology by analyzing performance traces of over 13 million requests to Facebook servers We present a detailed study of the factors that affect the end-to-end latency of such requests We also use our methodology to suggest and validate a scheduling optimization for improving Facebook request latency

Facial performance sensing head-mounted display

Abstract: There are currently no solutions for enabling direct face-to-face interaction between virtual reality (VR) users wearing head-mounted displays (HMDs) The main challenge is that the headset obstructs a significant portion of a user's face, preventing effective facial capture with traditional techniques To advance virtual reality as a next-generation communication platform, we develop a novel HMD that enables 3D facial performance-driven animation in real-time Our wearable system uses ultra-thin flexible electronic materials that are mounted on the foam liner of the headset to measure surface strain signals corresponding to upper face expressions These strain signals are combined with a head-mounted RGB-D camera to enhance the tracking in the mouth region and to account for inaccurate HMD placement To map the input signals to a 3D face model, we perform a single-instance offline training session for each person For reusable and accurate online operation, we propose a short calibration step to readjust the Gaussian mixture distribution of the mapping before each use The resulting animations are visually on par with cutting-edge depth sensor-driven facial performance capture systems and hence, are suitable for social interactions in virtual worlds