Background
oPRO.ai is built on years of groundbreaking work on machine learning and distributed computing.
Publications
Overview
oPRO.ai: A New Platform for Distributed Machine Learning on Big Data
IEEE Transactions on Big Data, Volume 1, No. 2, Pages 49-67, 2015
Systems
Strategies and Principles of Distributed Machine Learning on Big Data
Engineering, Volume 2, No. 2, Pages 179-95, 2016
Orpheus: Efficient Distributed Machine Learning via System and Algorithm Co-design
Symposium of Cloud Computing (SoCC 2018)
Poseidon: An Efficient Communication Interface for Distributed Deep Learning on GPU Clusters
USENIX Annual Technical Conference (ATC 2017)
STRADS: A Distributed Framework for Scheduled Model Parallel Machine Learning
European Conference on Computer Systems (Eurosys 2016)
Managed Communication and Consistency for Fast Data-Parallel Iterative Analytics
ACM Symposium on Cloud Computing (SOCC 2015), Best paper award.
More Effective Distributed ML via a Stale Synchronous Parallel Parameter Server
Neural Information Processing Systems (NIPS 2013), Oral (top 5%)
On Model Parallelization and Scheduling Strategies for Distributed Machine Learning
Neural Information Processing Systems (NIPS 2014)
Theory
On Convergence of Model Parallel Proximal Gradient Algorithm for Stale Synchronous Parallel System
Artificial Intelligence and Statistics (AISTATS 2016)
Analysis of High-Performance Distributed ML at Scale Through Parameter Server Consistency Models
AAAI Conference on Artificial Intelligence (AAAI 2015)
Lighter-Communication Distributed Machine Learning via Sufficient Factor Broadcasting
Uncertainty in Artificial Intelligence (UAI 2016)
Other Applications
Harnessing Deep Neural Networks with Logic Rules
Annual Meeting of the Association for Computational Linguistics (ACL 2016) | Outstanding Paper Award.
Learning Answer-Entailing Structures for Machine Comprehension
Annual Meeting of the Association for Computational Linguistics (ACL 2015) | Honorable Mention Recipient.
ZM-Net: Real-time Zero-shot Image Manipulation Network
Currently under submission.
Diversity-Promoting Bayesian Learning of Latent Variable Models
International Conference on Machine Learning (ICML 2016)
Science Question Answering Using Instructional Materials
Annual Meeting of the Association for Computational Linguistics (ACL 2016)
Learning Concept Taxonomies from Multi-Modal Data
Annual Meeting of the Association for Computational Linguistics (ACL 2016)
Grounding Topic Models with Knowledge Bases
International Joint Conference on Artificial Intelligence (IJCAI 2016)
Presentations
A Statistical Machine Learning Perspective of Deep Learning: Algorithm, Theory, Scalable Computing
International Summer School on Deep Learning, Bilbao, Spain
System and Algorithm Co-Design, Theory and Practice, for Distributed Machine Learning
Simons Institute for the Theory of Computing
High Efficiency Systems for Distributed AI and ML at Scale
Strata+Hadoop World in Singapore, 2016