Tess E. Smidt

Publication Activity (10 Years)

Years Active: 2020-2024
Publications (10 Years): 17

Top Topics

Symmetry Breaking

Top Venues

Mach. Learn. Sci. Technol.

J. Mach. Learn. Res.

Publications

Yuqing Xie, Tess E. Smidt
Equivariant Symmetry Breaking Sets. CoRR (2024)
Joshua A. Rackers, Lucas Tecot, Mario Geiger, Tess E. Smidt
A recipe for cracking the quantum scaling limit with machine learned electron densities. Mach. Learn. Sci. Technol. 4 (1) (2023)
Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka
Sign and Basis Invariant Networks for Spectral Graph Representation Learning. ICLR (2023)
Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner
A General Framework for Equivariant Neural Networks on Reductive Lie Groups. NeurIPS (2023)
Rui Wang, Robin Walters, Tess E. Smidt
Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems. CoRR (2023)
Allan dos Santos Costa, Ilan Mitnikov, Mario Geiger, Manvitha Ponnapati, Tess E. Smidt, Joseph Jacobson
Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders. CoRR (2023)
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 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
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR (2023)
Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt
Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation. CoRR (2023)
Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner
A General Framework for Equivariant Neural Networks on Reductive Lie Groups. CoRR (2023)
Yi-Lun Liao, Brandon M. Wood, Abhishek Das, Tess E. Smidt
EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. CoRR (2023)
Yi-Lun Liao, Tess E. Smidt
Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. ICLR (2023)
Alice Gatti, Zhixiong Hu, Tess E. Smidt, Esmond G. Ng, Pieter Ghysels
Deep Learning and Spectral Embedding for Graph Partitioning. PPSC (2022)
Alice Gatti, Zhixiong Hu, Tess E. Smidt, Esmond G. Ng, Pieter Ghysels
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks. J. Mach. Learn. Res. 23 (2022)
Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli
Generative Coarse-Graining of Molecular Conformations. ICML (2022)
Simon L. Batzner, Tess E. Smidt, Lixin Sun, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Boris Kozinsky
SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials. CoRR (2021)
Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé
Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. CoRR (2020)
Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks. CoRR (2020)