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 Invariant Representations for Reinforcement Learning without Reconstruction

Abstract: We study how representation learning can accelerate reinforcement learning from rich observations, such as images, without relying either on domain knowledge or pixel-reconstruction. Our goal is to learn representations that both provide for effective downstream control and invariance to task-irrelevant details. Bisimulation metrics quantify behavioral similarity between states in continuous MDPs, which we propose using to learn robust latent representations which encode only the task-relevant information from observations. Our method trains encoders such that distances in latent space equal bisimulation distances in state space. We demonstrate the effectiveness of our method at disregarding task-irrelevant information using modified visual MuJoCo tasks, where the background is replaced with moving distractors and natural videos, while achieving SOTA performance. We also test a first-person highway driving task where our method learns invariance to clouds, weather, and time of day. Finally, we provide generalization results drawn from properties of bisimulation metrics, and links to causal inference.

The Role of Social Networks in Information Diffusion

Abstract: Online social networking technologies enable individuals to simultaneously share information with any number of peers. Quantifying the causal effect of these technologies on the dissemination of information requires not only identification of who influences whom, but also of whether individuals would still propagate information in the absence of social signals about that information. We examine the role of social networks in online information diffusion with a large-scale field experiment that randomizes exposure to signals about friends' information sharing among 253 million subjects in situ. Those who are exposed are significantly more likely to spread information, and do so sooner than those who are not exposed. We further examine the relative role of strong and weak ties in information propagation. We show that, although stronger ties are individually more influential, it is the more abundant weak ties who are responsible for the propagation of novel information. This suggests that weak ties may play a more dominant role in the dissemination of information online than currently believed.

Detect-and-Track: Efficient Pose Estimation in Videos

Abstract: This paper addresses the problem of estimating and tracking human body keypoints in complex, multi-person video. We propose an extremely lightweight yet highly effective approach that builds upon the latest advancements in human detection [17] and video understanding [5]. Our method operates in two-stages: keypoint estimation in frames or short clips, followed by lightweight tracking to generate keypoint predictions linked over the entire video. For frame-level pose estimation we experiment with Mask R-CNN, as well as our own proposed 3D extension of this model, which leverages temporal information over small clips to generate more robust frame predictions. We conduct extensive ablative experiments on the newly released multi-person video pose estimation benchmark, PoseTrack, to validate various design choices of our model. Our approach achieves an accuracy of 55.2% on the validation and 51.8% on the test set using the Multi-Object Tracking Accuracy (MOTA) metric, and achieves state of the art performance on the ICCV 2017 PoseTrack keypoint tracking challenge [1].

Facilitating interaction among users of a social network

Abstract: In one embodiment, a method includes receiving a first user action relating to a first topic from a first user, identifying the first topic based on the first user action, identifying one or more second posts that relate to the first topic, and transmitting to the first user one or more of the second posts or information associated with the second posts in a structured document for display to the first user, the structured document further comprising one or more interactive elements that enable the first user to interact with the one or more second posts or to respective second users that declared the second posts.

Providing relevant notifications 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.