3.8k citations · 8.2k across the 13 of their papers we have counts for
3 papers · 1 filter
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Li Li, Stephan Hoyer, Ryan Pederson +4
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectur…
Scaling Symbolic Methods using Gradients for Neural Model Explanation
Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li +2
Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly li…
Machine learning on DNA-encoded libraries: A new paradigm for hit-finding
Kevin McCloskey, Eric A. Sigel, Steven Kearnes +16
DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value through screening of libraries with up…