Publications

May 4, 2019

International Conference on Learning Representations (ICLR)

Momentum-based acceleration of stochastic gradient descent (SGD) is widely used in deep learning. We propose the quasi-hyperbolic momentum algorithm (QHM) as an extremely simple alteration of momentum SGD, averaging a plain SGD step with a momentum step. We describe numerous connections to and identities with other algorithms, and we characterize the set of two-state optimization algorithms that QHM can recover.

Areas: Computer Vision Facebook AI Research Machine Learning Natural Language Processing & Speech

February 16, 2019

IEEE International Symposium on High-Performance Computer Architecture (HPCA)

This paper takes a data-driven approach to present the opportunities and design challenges faced by Facebook in order to enable machine learning inference locally on smartphones and other edge platforms.

Areas: AR/VR Machine Learning Systems & Networking

January 30, 2019

AAAI Conference on Artificial Intelligence (AAAI)

Large scale visual understanding is challenging, as it requires a model to handle the widely-spread and imbalanced distribution of triples. In real-world scenarios with large numbers of objects and relations, some are seen very commonly while others are barely seen. We develop a new relationship detection model that embeds objects and relations into two vector spaces where both discriminative capability and semantic affinity are preserved.

Areas: Computer Vision Facebook AI Research Machine Learning

December 14, 2018

We introduce PyText – a deep learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale.

Areas: Facebook AI Research Machine Learning Natural Language Processing & Speech

December 8, 2018

Integration of Deep Learning Theories Workshop at NeurIPS

We derive a simple and model-independent formula for the change in the generalization gap due to a gradient descent update. We then compare the change in the test error for stochastic gradient descent to the change in test error from an equivalent number of gradient descent updates and show explicitly that stochastic gradient descent acts to regularize generalization error by decorrelating nearby updates.

Areas: Facebook AI Research Machine Learning

December 7, 2018

Bayesian Deep Learning Workshop at NeurIPS 2018

In this study, we present HackPPL as a probabilistic programming language in Facebook’s server-side language, Hack. One of the aims of our language is to support deep probabilistic modeling by providing a flexible interface for composing deep neural networks with encoded uncertainty and a rich inference engine.

Areas: Machine Learning

December 7, 2018

Causal Learning Workshop at NeurIPS

We propose to combine reinforcement learning and theoretical physics to describe effective theories of agency. This involves understanding the connection between the physics notion of causality and how intelligent agents can arise as a useful effective description within some environments.

Areas: Facebook AI Research Machine Learning

December 7, 2018

Systems for Machine Learning Workshop at NeurIPS 2018

We improve floating point to be more energy efficient than equivalent bit width integer hardware on a 28 nm ASIC process while retaining accuracy in 8 bits with a novel hybrid log multiply/linear add, Kulisch accumulation and tapered encodings from Gustafson’s posit format.

Areas: Facebook AI Research Machine Learning Systems & Networking

December 6, 2018

Neural Information Processing Systems (NeurIPS)

Optimization algorithms that leverage gradient covariance information, such as variants of natural gradient descent (Amari, 1998), offer the prospect of yielding more effective descent directions. For models with many parameters, the covariance matrix they are based on becomes gigantic, making them inapplicable in their original form.

Areas: Facebook AI Research Machine Learning

December 4, 2018

ACM SIGGRAPH Asia 2018

In this paper, we introduce DeepFocus, a generic, end-to-end convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using only commonly available RGB-D images, enabling real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.

Areas: AR/VR Machine Learning