Built on Groundbreaking Research.
Major Petuum innovations include the parameter server architecture and theory, the stale-synchronous parallel bridging model, elastic resource scheduling technology, and managed network communication technology in high-performance distributed systems for ML and DL – as well as new ML and DL models and algorithms for actionable, human-level understanding and learning from images, videos and natural language.
Strategies and Principles of Distributed Machine Learning on Big Data
Engineering, Volume 2, No. 2, Pages 179-95, 2016
Petuum: A New Platform for Distributed Machine Learning on Big Data
IEEE Transactions on Big Data, Volume 1, No. 2, Pages 49-67, 2015
Connecting the Dots Between MLE and RL for Sequence Generation
AutoLoss: Learning Discrete Schedules for Alternate Optimization
Predicting Discharge Medications at Admission Time Based on Deep Learning
Towards Automated ICD Coding Using Deep Learning
Medical Diagnosis from Laboratory Tests by Combining Generative and Discriminative Learning
Effective Use of Bidirectional Language Modeling for Medical Named Entity Recognition
Proceedings of Machine Learning Research