Liang Xiong
Liang studied for his Ph.D. in Machine Learning at CMU before joining Facebook as a Research Manager. He builds cutting-edge AI to understand, inspire, and connect people to what's most meaningful. He develops algorithms and systems to learn everything about and around people and optimizes services to help them achieve what they care about. Liang's work involves ranking, retrieval, deep learning, sparse/graph/sequence modeling, reinforcement learning, optimization, large-scale training, and ML platform. His work intersects cutting-edge research and massive real-world applications and impact.
Liang's Work
Liang's Publications
December 23, 2020
RESEARCH
NLP
Variational Training for Large-Scale Noisy-OR Bayesian Networks
We propose a stochastic variational inference algorithm for training large-scale Bayesian networks, where noisy-OR conditional distributions are used to capture higher-order relationships. One application is to the learning of hierarchical…
Geng Ji, Dehua Cheng, Huazhong Ning, Changhe Yuan, Hanning Zhou, Liang Xiong, Erik B. Sudderth,
December 23, 2020
December 23, 2020
RESEARCH
COMPUTER VISION
Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective
Machine learning sits at the core of many essential products and services at Facebook. This paper describes the hardware and software infrastructure that supports machine learning at global scale. Facebook’s machine learning workloads are…
Kim Hazelwood, Sarah Bird, David Brooks, Soumith Chintala, Utku Diril, Dmytro Dzhulgakov, Mohamed Fawzy, Bill Jia, Yangqing Jia, Aditya Kalro, James Law, Kevin Lee, Jason Lu, Pieter Noordhuis, Misha Smelyanskiy, Liang Xiong, Xiaodong Wang,
December 23, 2020
December 23, 2020
RESEARCH
The Architectural Implications of Facebook’s DNN-based Personalized Recommendation
The widespread application of deep learning has changed the landscape of computation in data centers. In particular, personalized recommendation for content ranking is now largely accomplished using deep neural networks. However, despite their…
Udit Gupta, Carole-Jean Wu, Xiaodong Wang, Maxim Naumov, Brandon Reagen, David Brooks, Bradford Cottel, Kim Hazelwood, Mark Hempstead, Bill Jia, Hsien-Hsin S. Lee, Andrey Malevich, Dheevatsa Mudigere, Mikhail Smelyanskiy, Liang Xiong, Xuan Zhang,
December 23, 2020
