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.

DeepWrinkles: Accurate and Realistic Clothing Modeling

Abstract: We present a novel method to generate accurate and realistic clothing deformation from real data capture. Previous methods for realistic cloth modeling mainly rely on intensive computation of physics-based simulation (with numerous heuristic parameters), while models reconstructed from visual observations typically suffer from lack of geometric details. Here, we propose an original framework consisting of two modules that work jointly to represent global shape deformation as well as surface details with high fidelity. Global shape deformations are recovered from a subspace model learned from 3D data of clothed people in motion, while high frequency details are added to normal maps created using a conditional Generative Adversarial Network whose architecture is designed to enforce realism and temporal consistency. This leads to unprecedented high-quality rendering of clothing deformation sequences, where fine wrinkles from (real) high resolution observations can be recovered. In addition, as the model is learned independently from body shape and pose, the framework is suitable for applications that require retargeting (e.g., body animation). Our experiments show original high quality results with a flexible model. We claim an entirely data-driven approach to realistic cloth wrinkle generation is possible.

Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills

Abstract: Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent. Previous work has introduced tasks and datasets that aim to help agents to learn those qualities in isolation and gauge how well they can express them. But rather than being specialized in one single quality, a good open-domain conversational agent should be able to seamlessly blend them all into one cohesive conversational flow. In this work, we investigate several ways to combine models trained towards isolated capabilities, ranging from simple model aggregation schemes that require minimal additional training, to various forms of multi-task training that encompass several skills at all training stages. We further propose a new dataset, BlendedSkillTalk, to analyze how these capabilities would mesh together in a natural conversation, and compare the performance of different architectures and training schemes. Our experiments show that multi-tasking over several tasks that focus on particular capabilities results in better blended conversation performance compared to models trained on a single skill, and that both unified or two-stage approaches perform well if they are constructed to avoid unwanted bias in skill selection or are fine-tuned on our new task.

Leveraging Information in a Social Network for Inferential Targeting of Advertisements

Abstract: A social network targets advertisements to its members using inferential ad targeting An inferential ad enables advertisers to reach members that do not meet targeting criteria for lack of information A member's connections in the social network that satisfy the targeting criteria are leveraged to infer a targeted interest An inferential ad is selected from a candidate set to be presented to the member Varying complexities of targeting criteria, secondary inferential targeting criteria, and scopes of inference provide flexibility for inferential ad targeting in a social network

Evaluating Prerequisite Qualities for Learning End-to-End Dialog Systems

Abstract: A long-term goal of machine learning is to build intelligent conversational agents. One recent popular approach is to train end-to-end models on a large amount of real dialog transcripts between humans (Sordoni et al., 2015; Vinyals & Le, 2015; Shang et al., 2015). However, this approach leaves many questions unanswered as an understanding of the precise successes and shortcomings of each model is hard to assess. A contrasting recent proposal are the bAbI tasks (Weston et al., 2015b) which are synthetic data that measure the ability of learning machines at various reasoning tasks over toy language. Unfortunately, those tests are very small and hence may encourage methods that do not scale. In this work, we propose a suite of new tasks of a much larger scale that attempt to bridge the gap between the two regimes. Choosing the domain of movies, we provide tasks that test the ability of models to answer factual questions (utilizing OMDB), provide personalization (utilizing MovieLens), carry short conversations about the two, and finally to perform on natural dialogs from Reddit. We provide a dataset covering 75k movie entities and with 3.5M training examples. We present results of various models on these tasks, and evaluate their performance.

Investment in Two Sided Markets and the Net Neutrality Debate

Abstract: This paper develops a game theoretic model based on a two-sided market framework to investigate the net neutrality debate. In particular, we consider investment incentives of Internet Service Providers (ISPs) under a neutral and non-neutral network regimes. In our model, two interconnected ISPs compete over quality and prices for heterogeneous content providers (CPs) and heterogeneous consumers. We consider two definitions of a non-neutral network: in the first, ISPs charge access fees to off-network CPs; in the second, ISPs offer "priority lanes''. In the neutral regime, connecting to a single ISP allows a CP to communicate with all consumers and all CPs obtain the same quality of service. With a combination of theoretical and numerical results we find that under both definitions ISPs' quality-investment levels are driven by the trade-off they make between softening price competition on the consumer side and increasing revenues extracted from CPs. Specifically, in the non-neutral regime, because it is easier to extract surplus through appropriate CP pricing, ISPs' investment levels are larger. Because CP quality is enhanced by the quality of ISPs, larger investment levels imply that CPs' revenues increase. Similarly, consumer surplus increases as well. The main insight resulting from our model is that social welfare is larger in the non-neutral regime. Our results highlight important mechanisms related to ISPs' investments that play a key role in market outcomes, providing useful insights that enrich the net neutrality debate.