Search Results for author: Nathan C. Frey

Found 10 papers, 3 papers with code

A Green(er) World for A.I

no code implementations27 Jan 2023 Dan Zhao, Nathan C. Frey, Joseph McDonald, Matthew Hubbell, David Bestor, Michael Jones, Andrew Prout, Vijay Gadepally, Siddharth Samsi

applications, we are sure to face an ever-mounting energy footprint to sustain these computational budgets, data storage needs, and more.

Graph Contrastive Learning for Materials

no code implementations24 Nov 2022 Teddy Koker, Keegan Quigley, Will Spaeth, Nathan C. Frey, Lin Li

By leveraging a series of material-specific transformations, we introduce CrystalCLR, a framework for constrastive learning of representations with crystal graph neural networks.

Contrastive Learning

FastFlows: Flow-Based Models for Molecular Graph Generation

1 code implementation28 Jan 2022 Nathan C. Frey, Vijay Gadepally, Bharath Ramsundar

We propose a framework using normalizing-flow based models, SELF-Referencing Embedded Strings, and multi-objective optimization that efficiently generates small molecules.

Graph Generation Molecular Graph Generation

Benchmarking Resource Usage for Efficient Distributed Deep Learning

no code implementations28 Jan 2022 Nathan C. Frey, Baolin Li, Joseph McDonald, Dan Zhao, Michael Jones, David Bestor, Devesh Tiwari, Vijay Gadepally, Siddharth Samsi

Deep learning (DL) workflows demand an ever-increasing budget of compute and energy in order to achieve outsized gains.


Scalable Geometric Deep Learning on Molecular Graphs

1 code implementation NeurIPS Workshop AI4Scien 2021 Nathan C. Frey, Siddharth Samsi, Joseph McDonald, Lin Li, Connor W. Coley, Vijay Gadepally

Deep learning in molecular and materials sciences is limited by the lack of integration between applied science, artificial intelligence, and high-performance computing.

The Pseudo Projection Operator: Applications of Deep Learning to Projection Based Filtering in Non-Trivial Frequency Regimes

no code implementations13 Nov 2021 Matthew L. Weiss, Nathan C. Frey, Siddharth Samsi, Randy C. Paffenroth, Vijay Gadepally

Traditional frequency based projection filters, or projection operators (PO), separate signal and noise through a series of transformations which remove frequencies where noise is present.


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