This work shows how to leverage causal inference to understand the behavior of complex learning systems interacting with their environment and predict the consequences of changes to the system. Such p...
For low resource languages, collecting sufficient training data to build acoustic and language models is time consuming and often expensive. In this paper, we investigate the use of online text resour...
This paper examines the workload of Facebook’s photo-serving stack and the effectiveness of the many layers of caching it employs. Facebook’s image-management infrastructure is complex and geographically distributed. It includes browser caches on end-user systems, Edge Caches at ~20 PoPs, an Origin Cache, and for some kinds of images, additional caching via Akamai. The underlying image storage layer is widely distributed, and includes multiple data centers.
Today’s cloud network platforms allow tenants to construct sophisticated virtual network topologies among their VMs on a shared physical network infrastructure. However, these platforms provide little...
Facebook takes performance monitoring seriously. Performance issues can impact over one billion users so we track thousands of servers, hundreds of PB of daily network traffic, hundreds of daily code...
Distributed storage systems for large clusters typically use replication to provide reliability. Recently, erasure codes have been used to reduce the large storage overhead of three-replicated systems. Reed-Solomon codes are the standard design choice and their high repair cost is often considered an unavoidable price to pay for high storage efficiency and high reliability.
Unicorn is an online, in-memory social graph-aware indexing system designed to search trillions of edges between tens of billions of users and entities on thousands of...
Similarity search, or finding approximate nearest neighbors, is an important technique for many applications. Many recent research demonstrate that hashing methods can achieve promising results for large scale similarity search due to its computational and memory efficiency.
Recent years have witnessed the success of hashing techniques in approximate nearest neighbor search. In practice, multiple hash tables are usually employed to retrieve more desired results from all hit buckets of each table. However, there are rare works studying the unified approach to constructing multiple informative hash tables except the widely used random way.
Slow convergence and poor initial accuracy are two problems that plague efforts to use very large feature sets in online learning. This is especially true when only a few features are 'active' in any...
