Nathan C. Frey | DeepAI

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Kyunghyun Cho
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∙ 05/31/2023

Protein Design with Guided Discrete Diffusion

A popular approach to protein design is to combine a generative model wi...

3 Nate Gruver, et al. ∙

∙ 02/15/2023

SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers

We investigate Siamese networks for learning related embeddings for augm...

2 Michael Maser, et al. ∙

∙ 01/27/2023

A Green(er) World for A.I

As research and practice in artificial intelligence (A.I.) grow in leaps...

0 Dan Zhao, et al. ∙

∙ 11/24/2022

Graph Contrastive Learning for Materials

Recent work has shown the potential of graph neural networks to efficien...

0 Teddy Koker, et al. ∙

∙ 10/19/2022

A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences

Deep generative models have emerged as a popular machine learning-based ...

8 Natasa Tagasovska, et al. ∙

∙ 03/31/2022

SELFIES and the future of molecular string representations

Artificial intelligence (AI) and machine learning (ML) are expanding in ...

0 Mario Krenn, et al. ∙

∙ 01/28/2022

Benchmarking Resource Usage for Efficient Distributed Deep Learning

Deep learning (DL) workflows demand an ever-increasing budget of compute...

0 Nathan C. Frey, et al. ∙

∙ 01/28/2022

FastFlows: Flow-Based Models for Molecular Graph Generation

We propose a framework using normalizing-flow based models, SELF-Referen...

0 Nathan C. Frey, et al. ∙

∙ 12/09/2021

Bringing Atomistic Deep Learning to Prime Time

Artificial intelligence has not yet revolutionized the design of materia...

21 Nathan C. Frey, et al. ∙

∙ 12/06/2021

Scalable Geometric Deep Learning on Molecular Graphs

Deep learning in molecular and materials sciences is limited by the lack...

3 Nathan C. Frey, et al. ∙

∙ 11/13/2021

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

Traditional frequency based projection filters, or projection operators ...

0 Matthew L Weiss, et al. ∙

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