Aditi S. Krishnapriyan

Orcid: 0000-0003-3472-6080

According to our database1, Aditi S. Krishnapriyan authored at least 19 papers between 2020 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains.
CoRR, 2024

General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design.
CoRR, 2024

Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement.
CoRR, 2024

Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators.
CoRR, 2024

Topological regularization via persistence-sensitive optimization.
Comput. Geom., 2024

Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Neural Spectral Methods: Self-supervised learning in the spectral domain.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Scaling physics-informed hard constraints with mixture-of-experts.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Chemical reaction networks and opportunities for machine learning.
Nat. Comput. Sci., 2023

Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations.
CoRR, 2023

CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation.
CoRR, 2023

Learning differentiable solvers for systems with hard constraints.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Learning continuous models for continuous physics.
CoRR, 2022

AutoIP: A United Framework to Integrate Physics into Gaussian Processes.
Proceedings of the International Conference on Machine Learning, 2022

2021
Characterizing possible failure modes in physics-informed neural networks.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction.
CoRR, 2020

Persistent homology advances interpretable machine learning for nanoporous materials.
CoRR, 2020

Robust Topological Descriptors for Machine Learning Prediction of Guest Adsorption in Nanoporous Materials.
CoRR, 2020


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