Tess E. Smidt

According to our database, Tess E. Smidt authored at least 29 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing.

Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lázaro, Tommi S. Jaakkola, Tess E. Smidt

CoRR, 2024

EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants.

Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini, Ameya Daigavane, Mario Geiger, Zhonglin Cao, Karsten Kreis, Tess E. Smidt, Emine Küçükbenli, Joseph Jacobson

CoRR, 2024

Relaxed Equivariant Graph Neural Networks.

Elyssa F. Hofgard, Rui Wang, Robin Walters, Tess E. Smidt

CoRR, 2024

A Recipe for Charge Density Prediction.

Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola

CoRR, 2024

Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields.

Yi-Lun Liao, Tess E. Smidt, Abhishek Das

CoRR, 2024

Equivariant Symmetry Breaking Sets.

Yuqing Xie, Tess E. Smidt

CoRR, 2024

Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution.

Rui Wang, Elyssa F. Hofgard, Hang Gao, Robin Walters, Tess E. Smidt

Proceedings of the Forty-first International Conference on Machine Learning, 2024

EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations.

Yi-Lun Liao, Brandon M. Wood, Abhishek Das, Tess E. Smidt

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation.

Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

A recipe for cracking the quantum scaling limit with machine learned electron densities.

Joshua A. Rackers, Lucas Tecot, Mario Geiger, Tess E. Smidt

Mach. Learn. Sci. Technol., March, 2023

Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation.

Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt

CoRR, 2023

Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders.

Allan dos Santos Costa, Ilan Mitnikov, Mario Geiger, Manvitha Ponnapati, Tess E. Smidt, Joseph Jacobson

CoRR, 2023

Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems.

Rui Wang, Robin Walters, Tess E. Smidt

CoRR, 2023

Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems.

Xuan Zhang, Limei Wang, Jacob Helwig, Youzhi Luo, Cong Fu, Yaochen Xie, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

CoRR, 2023

A General Framework for Equivariant Neural Networks on Reductive Lie Groups.

Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Sign and Basis Invariant Networks for Spectral Graph Representation Learning.

Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs.

Yi-Lun Liao, Tess E. Smidt

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks.

Alice Gatti, Zhixiong Hu, Tess E. Smidt, Esmond G. Ng, Pieter Ghysels

J. Mach. Learn. Res., 2022

Learning Integrable Dynamics with Action-Angle Networks.

Ameya Daigavane, Arthur Kosmala, Miles D. Cranmer, Tess E. Smidt, Shirley Ho

CoRR, 2022

e3nn: Euclidean Neural Networks.

Mario Geiger, Tess E. Smidt

CoRR, 2022

Deep Learning and Spectral Embedding for Graph Partitioning.

Alice Gatti, Zhixiong Hu, Tess E. Smidt, Esmond G. Ng, Pieter Ghysels

Proceedings of the 2022 SIAM Conference on Parallel Processing for Scientific Computing, 2022

Generative Coarse-Graining of Molecular Conformations.

Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli

Proceedings of the International Conference on Machine Learning, 2022

2021

Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning.

Alice Gatti, Zhixiong Hu, Pieter Ghysels, Esmond G. Ng, Tess E. Smidt

CoRR, 2021

SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials.

Simon L. Batzner, Tess E. Smidt, Lixin Sun, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Boris Kozinsky

CoRR, 2021

SE(3)-equivariant prediction of molecular wavefunctions and electronic densities.

Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess E. Smidt, Klaus-Robert Müller

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties.

Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé

CoRR, 2020

Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks.

Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller

CoRR, 2020

2018

Toward the Systematic Design of Complex Materials from Structural Motifs.

Tess E. Smidt

PhD thesis, 2018

Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds.

Nathaniel Thomas, Tess E. Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, Patrick Riley

CoRR, 2018