Research

In this work we ensemble several models and achieve state of the art accuracy for predicting the sentiment of movie reviews in the IMDB dataset.

In this work, we investigate models of natural high-resolution video sequences. We show that very simple models borrowed by language modeling applications are surprisingly effective at recovering shor...

We are interested in improving the quality and coverage of a knowledge graph through crowdsourcing features built into a social networking service. This work presents an approach to model user trust when prior history is lacking.

We describe two methods to collect translation pairs from public Facebook content. We use the extracted translation pairs as additional training data for machine translation systems and we can show significant improvements.

The HipHop Virtual Machine (HHVM) is a JIT compiler and runtime for PHP. While PHP values are dynamically typed, real programs often have latent types that are useful for optimization once discovered....

Facebook's corpus of photos, videos, and other Binary Large OBjects (BLOBs) that need to be reliably stored and quickly accessible is massive and continues to grow.

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 softwar...

With the advent of increased computing on mobile devices such as phones and tablets, it has become crucial to pay attention to the energy consumption of mobile applications.

Poselets have been used in a variety of computer vision tasks, such as detection, segmentation, action classification, pose estimation and action recognition, often achieving state-of-the-art performa...

This paper presents a system which learns to answer questions on a broad range of topics from a knowledge base using few handcrafted features. Our model learns low-dimensional embeddings of words and knowledge base constituents; these representations are used to score natural language questions against candidate answers.