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ORCIDAditi S. Krishnapriyan
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2020 – today
- 2025
[j4]Danny Reidenbach
, Aditi S. Krishnapriyan:
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation. J. Chem. Inf. Model. 65(1): 22-30 (2025)- [j3]Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan:
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators. Trans. Mach. Learn. Res. 2025 (2025) - [c10]Ishan Amin, Sanjeev Raja, Aditi S. Krishnapriyan:
Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians. ICLR 2025 - [c9]Sanjeev Raja, Martin Sípka, Michael Psenka, Tobias Kreiman, Michal Pavelka, Aditi S. Krishnapriyan:
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional. ICML 2025 - [i25]Ishan Amin, Sanjeev Raja, Aditi S. Krishnapriyan:
Towards Fast, Specialized Machine Learning Force Fields: Distilling Foundation Models via Energy Hessians. CoRR abs/2501.09009 (2025) - [i24]Tobias Kreiman, Aditi S. Krishnapriyan:
Understanding and Mitigating Distribution Shifts For Machine Learning Force Fields. CoRR abs/2503.08674 (2025) - [i23]Sanjeev Raja, Martin Sípka, Michael Psenka, Tobias Kreiman, Michal Pavelka, Aditi S. Krishnapriyan:
Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional. CoRR abs/2504.18506 (2025) - [i22]Yuan Chiang, Tobias Kreiman, Christine Zhang, Matthew C. Kuner, Elizabeth Weaver, Ishan Amin, Hyunsoo Park, Yunsung Lim, Jihan Kim, Daryl Chrzan, Aron Walsh, Samuel M. Blau, Mark Asta, Aditi S. Krishnapriyan:
MLIP Arena: Advancing Fairness and Transparency in Machine Learning Interatomic Potentials via an Open, Accessible Benchmark Platform. CoRR abs/2509.20630 (2025) - [i21]Tobias Kreiman, Yutong Bai, Fadi Atieh, Elizabeth Weaver, Eric Qu, Aditi S. Krishnapriyan:
Transformers Discover Molecular Structure Without Graph Priors. CoRR abs/2510.02259 (2025) - [i20]Yiheng Du, Aditi S. Krishnapriyan:
EddyFormer: Accelerated Neural Simulations of Three-Dimensional Turbulence at Scale. CoRR abs/2510.24173 (2025) - 2024
- [j2]Arnur Nigmetov, Aditi S. Krishnapriyan, Nicole Sanderson, Dmitriy Morozov:
Topological regularization via persistence-sensitive optimization. Comput. Geom. 120: 102086 (2024) - [c8]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 - [c7]Nithin Chalapathi, Yiheng Du, Aditi S. Krishnapriyan:
Scaling physics-informed hard constraints with mixture-of-experts. ICLR 2024 - [c6]Yiheng Du, Nithin Chalapathi, Aditi S. Krishnapriyan:
Neural Spectral Methods: Self-supervised learning in the spectral domain. ICLR 2024 - [c5]Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan:
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. ICLR 2024 - [c4]Eric Qu, Aditi S. Krishnapriyan:
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains. NeurIPS 2024 - [i19]Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan:
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. CoRR abs/2401.10216 (2024) - [i18]Nithin Chalapathi, Yiheng Du, Aditi S. Krishnapriyan:
Scaling physics-informed hard constraints with mixture-of-experts. CoRR abs/2402.13412 (2024) - [i17]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) - [i16]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) - [i15]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) - [i14]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) - [i13]Peter Wu, Bohan Yu, Kevin Scheck, Alan W. Black, Aditi S. Krishnapriyan, Irene Y. Chen, Tanja Schultz, Shinji Watanabe, Gopala Krishna Anumanchipalli:
Deep Speech Synthesis from Multimodal Articulatory Representations. CoRR abs/2412.13387 (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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