Explore the latest research from Facebook

We propose a novel framework to identify subgoals useful for exploration in sequential decision making tasks under partial observability.

We consider a multi-agent framework for distributed optimization where each agent has access to a local smooth strongly convex function, and the collective goal is to achieve consensus on the parameters that minimize the sum of the agents’ local functions. We propose an algorithm wherein each agent operates asynchronously and independently of the other agents.

In this paper, we propose to leverage recent approaches in unsupervised machine translation to train a fully unsupervised neural transcompiler. We train our model on source code from open source GitHub projects, and show that it can translate functions between C++, Java, and Python with high accuracy.

By extending recent advances in vision-based tracking and physically based animation, we present the first algorithm capable of tracking high-fidelity hand deformations through highly self-contacting and self-occluding hand gestures, for both single hands and two hands.

Embodied agents operating in human spaces must be able to master how their environment works: what objects can the agent use, and how can it use them? We introduce a reinforcement learning approach for exploration for interaction, whereby an embodied agent autonomously discovers the affordance landscape of a new unmapped 3D environment (such as an unfamiliar kitchen).

We present Twine, Facebook’s cluster management system which has been running in production for the past decade. Twine has helped convert our infrastructure from a collection of siloed pools of customized machines dedicated to individual workloads, into a large-scale shared infrastructure with fungible hardware.

This paper introduces FlightTracker, a family of APIs and systems which now manage consistency for online access to Facebook’s graph. FlightTracker implicitly provides RYW and can be explicitly used to provide alternative consistency guarantees for special use cases; it enables flexible communication patterns between caches, which we have found important as the number of datacenters increases; it extends the same consistency guarantees to cross-shard indexes and materialized views, allowing us to transparently optimize queries; and it provides a uniform primitive for clients to obtain desired consistency guarantees across a variety of data stores.

Consensus-based replicated systems are complex, monolithic, and difficult to upgrade once deployed. As a result, deployed systems do not benefit from innovative research, and new consensus protocols rarely reach production. We propose virtualizing consensus by virtualizing the shared log API, allowing services to change consensus protocols without downtime.

This paper presents a different approach to cache development, successfully employed at Facebook, which extracts a core set of common requirements and functionality from otherwise disjoint caching systems. CacheLib is a general-purpose caching engine, designed based on experiences with a range of caching use cases at Facebook, that facilitates the easy development and maintenance of caches.

In this paper, we present the first Asynchronous Distributed Key Generation (ADKG) algorithm which is also the first distributed key generation algorithm that can generate cryptographic keys with a dual (𝑓, 2𝑓 + 1)−threshold (where 𝑓 is the number of faulty parties).