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RumbleML, a declarative machine learning framework
This piece introduces the need for and the benefits of RumbleML framework. Particularly, this framework addresses the shortcomings of contemporary ML frameworks of iterative nature by utilizing declarative paradigms instead.
Distributed Learning Systems with First-Order Methods
Monograph co-authored by Ce Zhang to be published in "Foundations and Trends in Databases"
Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development
Demo "Ease.ml/snoopy in Action: Towards Automatic Feasibility Analysis for Machine Learning Application Development" accepted at VLDB 2020
Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript
"Don't Waste Your Bits! Squeeze Activations and Gradients for Deep Neural Networks via TinyScript" accepted at ICML 2020