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ORCIDAditi S. Krishnapriyan
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2020 – today
- 2024
[j2]Arnur Nigmetov
, Aditi S. Krishnapriyan, Nicole Sanderson, Dmitriy Morozov:
Topological regularization via persistence-sensitive optimization. Comput. Geom. 120: 102086 (2024)- [c7]Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. AISTATS 2024: 2413-2421 - [c6]Nithin Chalapathi, Yiheng Du, Aditi S. Krishnapriyan:
Scaling physics-informed hard constraints with mixture-of-experts. ICLR 2024 - [c5]Yiheng Du, Nithin Chalapathi, Aditi S. Krishnapriyan:
Neural Spectral Methods: Self-supervised learning in the spectral domain. ICLR 2024 - [c4]Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan:
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. ICLR 2024 - [i18]Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan:
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. CoRR abs/2401.10216 (2024) - [i17]Nithin Chalapathi, Yiheng Du, Aditi S. Krishnapriyan:
Scaling physics-informed hard constraints with mixture-of-experts. CoRR abs/2402.13412 (2024) - [i16]Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan:
Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators. CoRR abs/2402.13984 (2024) - [i15]Hongwei Jin, Prasanna Balaprakash, Allen Zou, Pieter Ghysels, Aditi S. Krishnapriyan, Adam Mate, Arthur K. Barnes, Russell Bent:
Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement. CoRR abs/2405.10389 (2024) - [i14]Yue Jian, Curtis Wu, Danny Reidenbach, Aditi S. Krishnapriyan:
General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design. CoRR abs/2406.16821 (2024) - [i13]Eric Qu, Aditi S. Krishnapriyan:
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains. CoRR abs/2410.24169 (2024) - 2023
- [j1]Mingjian Wen, Evan Walter Clark Spotte-Smith, Samuel M. Blau, Matthew J. McDermott, Aditi S. Krishnapriyan, Kristin A. Persson:
Chemical reaction networks and opportunities for machine learning. Nat. Comput. Sci. 3(1): 12-24 (2023) - [c3]Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan:
Learning differentiable solvers for systems with hard constraints. ICLR 2023 - [i12]Danny Reidenbach, Aditi S. Krishnapriyan:
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation. CoRR abs/2306.14852 (2023) - [i11]Da Long, Wei W. Xing, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels. CoRR abs/2310.05387 (2023) - [i10]Daniel Rothchild, Andrew S. Rosen, Eric Taw, Connie Robinson, Joseph E. Gonzalez, Aditi S. Krishnapriyan:
Investigating the Behavior of Diffusion Models for Accelerating Electronic Structure Calculations. CoRR abs/2311.01491 (2023) - [i9]Yiheng Du, Nithin Chalapathi, Aditi S. Krishnapriyan:
Neural Spectral Methods: Self-supervised learning in the spectral domain. CoRR abs/2312.05225 (2023) - 2022
- [c2]Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
AutoIP: A United Framework to Integrate Physics into Gaussian Processes. ICML 2022: 14210-14222 - [i8]Aditi S. Krishnapriyan, Alejandro F. Queiruga, N. Benjamin Erichson, Michael W. Mahoney:
Learning continuous models for continuous physics. CoRR abs/2202.08494 (2022) - [i7]Da Long, Zheng Wang, Aditi S. Krishnapriyan, Robert M. Kirby, Shandian Zhe, Michael W. Mahoney:
AutoIP: A United Framework to Integrate Physics into Gaussian Processes. CoRR abs/2202.12316 (2022) - [i6]Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan:
Learning differentiable solvers for systems with hard constraints. CoRR abs/2207.08675 (2022) - 2021
- [c1]Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney:
Characterizing possible failure modes in physics-informed neural networks. NeurIPS 2021: 26548-26560 - [i5]Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney:
Characterizing possible failure modes in physics-informed neural networks. CoRR abs/2109.01050 (2021) - 2020
- [i4]Aditi S. Krishnapriyan, Maciej Haranczyk, Dmitriy Morozov:
Robust Topological Descriptors for Machine Learning Prediction of Guest Adsorption in Nanoporous Materials. CoRR abs/2001.05972 (2020) - [i3]Aditi S. Krishnapriyan, Joseph H. Montoya, Jens S. Hummelshøj, Dmitriy Morozov:
Persistent homology advances interpretable machine learning for nanoporous materials. CoRR abs/2010.00532 (2020) - [i2]Nicolas Swenson, Aditi S. Krishnapriyan, Aydin Buluç, Dmitriy Morozov, Katherine A. Yelick:
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction. CoRR abs/2010.16027 (2020) - [i1]Arnur Nigmetov, Aditi S. Krishnapriyan, Nicole Sanderson, Dmitriy Morozov:
Topological Regularization via Persistence-Sensitive Optimization. CoRR abs/2011.05290 (2020)
Coauthor Index
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