Aditi S. Krishnapriyan

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 26

Top Topics

Hard Constraints

Spectral Domain

Machine Learning

Diffusion Models

Top Venues

Publications

Arnur Nigmetov, Aditi S. Krishnapriyan, Nicole Sanderson, Dmitriy Morozov
Topological regularization via persistence-sensitive optimization. Comput. Geom. 120 (2024)
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)
Sanjeev Raja, Ishan Amin, Fabian Pedregosa, Aditi S. Krishnapriyan
Stability-Aware Training of Neural Network Interatomic Potentials with Differentiable Boltzmann Estimators. CoRR (2024)
Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. ICLR (2024)
Yiheng Du, Nithin Chalapathi, Aditi S. Krishnapriyan
Neural Spectral Methods: Self-supervised learning in the spectral domain. ICLR (2024)
Nithin Chalapathi, Yiheng Du, Aditi S. Krishnapriyan
Scaling physics-informed hard constraints with mixture-of-experts. ICLR (2024)
Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products. CoRR (2024)
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 (2024)
Nithin Chalapathi, Yiheng Du, Aditi S. Krishnapriyan
Scaling physics-informed hard constraints with mixture-of-experts. CoRR (2024)
Yue Jian, Curtis Wu, Danny Reidenbach, Aditi S. Krishnapriyan
General Binding Affinity Guidance for Diffusion Models in Structure-Based Drug Design. CoRR (2024)
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 (2023)
Yiheng Du, Nithin Chalapathi, Aditi S. Krishnapriyan
Neural Spectral Methods: Self-supervised learning in the spectral domain. CoRR (2023)
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) (2023)
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 (2023)
Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan
Learning differentiable solvers for systems with hard constraints. ICLR (2023)
Danny Reidenbach, Aditi S. Krishnapriyan
CoarsenConf: Equivariant Coarsening with Aggregated Attention for Molecular Conformer Generation. CoRR (2023)
Geoffrey Négiar, Michael W. Mahoney, Aditi S. Krishnapriyan
Learning differentiable solvers for systems with hard constraints. CoRR (2022)
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 (2022)
Aditi S. Krishnapriyan, Alejandro F. Queiruga, N. Benjamin Erichson, Michael W. Mahoney
Learning continuous models for continuous physics. CoRR (2022)
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)
Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney
Characterizing possible failure modes in physics-informed neural networks. CoRR (2021)
Aditi S. Krishnapriyan, Amir Gholami, Shandian Zhe, Robert M. Kirby, Michael W. Mahoney
Characterizing possible failure modes in physics-informed neural networks. NeurIPS (2021)
Aditi S. Krishnapriyan, Joseph Montoya, Jens S. Hummelshøj, Dmitriy Morozov
Persistent homology advances interpretable machine learning for nanoporous materials. CoRR (2020)
Arnur Nigmetov, Aditi S. Krishnapriyan, Nicole Sanderson, Dmitriy Morozov
Topological Regularization via Persistence-Sensitive Optimization. CoRR (2020)
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 (2020)
Aditi S. Krishnapriyan, Maciej Haranczyk, Dmitriy Morozov
Robust Topological Descriptors for Machine Learning Prediction of Guest Adsorption in Nanoporous Materials. CoRR (2020)