Tess E. Smidt - RankMe

50 papers A* 10Journal 38Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Asymptotically Fast Clebsch-Gordan Tensor Products with Vector Spherical Harmonics. CoRR Yuqing Xie, Ameya Daigavane, Mit Kotak, Tess E. Smidt
2026	J	`jnl`	PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials. CoRR Teddy Koker, Abhijeet Gangan, Mit Kotak, Jaime Marian, Tess E. Smidt
2025	J	`jnl`	A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models. CoRR Yuqing Xie, Tess E. Smidt
2025	J	`jnl`	Accelerating Protein Molecular Dynamics Simulation with DeepJump. CoRR Allan dos Santos Costa, Manvitha Ponnapati, Dana Rubin, Tess E. Smidt, Joseph Jacobson
2025	J	`jnl`	Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Found. Trends Mach. Learn. 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, Alex Strasser, Haiyang Yu, Yuqing Xie, Xiang Fu, 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
2025	J	`jnl`	High-performance training and inference for deep equivariant interatomic potentials. CoRR Chuin Wei Tan, Marc L. Descoteaux, Mit Kotak, Gabriel de Miranda Nascimento, Seán R. Kavanagh, Laura Zichi, Menghang Wang, Aadit Saluja, Yizhong R. Hu, Tess E. Smidt, Anders Johansson, William C. Witt, Boris Kozinsky, Albert Musaelian
2025	J	`jnl`	Implicit Augmentation from Distributional Symmetry in Turbulence Super-Resolution. CoRR Julia Balla, Jeremiah Bailey, Ali Backour, Elyssa F. Hofgard, Tommi S. Jaakkola, Tess E. Smidt, Ryley McConkey
2025	J	`jnl`	Matching the Optimal Denoiser in Point Cloud Diffusion with (Improved) Rotational Alignment. CoRR Ameya Daigavane, Yuqing Xie, Bodhi P. Vani, Saeed Saremi, Joseph Kleinhenz, Tess E. Smidt
2025	A*	`conf`	The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products. ICML Yuqing Xie, Ameya Daigavane, Mit Kotak, Tess E. Smidt
2025	J	`jnl`	The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products. CoRR Yuqing Xie, Ameya Daigavane, Mit Kotak, Tess E. Smidt
2025	J	`jnl`	To Augment or Not to Augment? Diagnosing Distributional Symmetry Breaking. CoRR Hannah Lawrence, Elyssa F. Hofgard, Vasco Portilheiro, Yuxuan Chen, Tess E. Smidt, Robin Walters
2025	J	`jnl`	Training a Foundation Model for Materials on a Budget. CoRR Teddy Koker, Tess E. Smidt
2024	J	`jnl`	A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing. CoRR Julia Balla, Siddharth Mishra-Sharma, Carolina Cuesta-Lázaro, Tommi S. Jaakkola, Tess E. Smidt
2024	A*	`conf`	A Recipe for Charge Density Prediction. NeurIPS Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola
2024	J	`jnl`	A Recipe for Charge Density Prediction. CoRR Xiang Fu, Andrew S. Rosen, Kyle Bystrom, Rui Wang, Albert Musaelian, Boris Kozinsky, Tess E. Smidt, Tommi S. Jaakkola
2024	A*	`conf`	Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution. ICML Rui Wang, Elyssa F. Hofgard, Hang Gao, Robin Walters, Tess E. Smidt
2024	J	`jnl`	EquiJump: Protein Dynamics Simulation via SO(3)-Equivariant Stochastic Interpolants. CoRR Allan dos Santos Costa, Ilan Mitnikov, Franco Pellegrini, Ameya Daigavane, Mario Geiger, Zhonglin Cao, Karsten Kreis, Tess E. Smidt, Emine Küçükbenli, Joseph Jacobson
2024	A*	`conf`	EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. ICLR Yi-Lun Liao, Brandon M. Wood, Abhishek Das, Tess E. Smidt
2024	J	`jnl`	Equivariant Symmetry Breaking Sets. CoRR Yuqing Xie, Tess E. Smidt
2024	J	`jnl`	Equivariant Symmetry Breaking Sets. Trans. Mach. Learn. Res. Yuqing Xie, Tess E. Smidt
2024	J	`jnl`	Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields. CoRR Yi-Lun Liao, Tess E. Smidt, Abhishek Das
2024	J	`jnl`	Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields. Trans. Mach. Learn. Res. Yi-Lun Liao, Tess E. Smidt, Muhammed Shuaibi, Abhishek Das
2024	J	`jnl`	Relaxed Equivariant Graph Neural Networks. CoRR Elyssa F. Hofgard, Rui Wang, Robin Walters, Tess E. Smidt
2024	A*	`conf`	Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation. ICLR Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt
2023	A*	`conf`	A General Framework for Equivariant Neural Networks on Reductive Lie Groups. NeurIPS Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner
2023	J	`jnl`	A General Framework for Equivariant Neural Networks on Reductive Lie Groups. CoRR Ilyes Batatia, Mario Geiger, Jose M. Munoz, Tess E. Smidt, Lior Silberman, Christoph Ortner
2023	J	`jnl`	A recipe for cracking the quantum scaling limit with machine learned electron densities. Mach. Learn. Sci. Technol. Joshua A. Rackers, Lucas Tecot, Mario Geiger, Tess E. Smidt
2023	J	`jnl`	Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. CoRR 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
2023	A*	`conf`	Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. ICLR Yi-Lun Liao, Tess E. Smidt
2023	J	`jnl`	EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations. CoRR Yi-Lun Liao, Brandon M. Wood, Abhishek Das, Tess E. Smidt
2023	J	`jnl`	Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders. CoRR Allan dos Santos Costa, Ilan Mitnikov, Mario Geiger, Manvitha Ponnapati, Tess E. Smidt, Joseph Jacobson
2023	J	`jnl`	Relaxed Octahedral Group Convolution for Learning Symmetry Breaking in 3D Physical Systems. CoRR Rui Wang, Robin Walters, Tess E. Smidt
2023	A*	`conf`	Sign and Basis Invariant Networks for Spectral Graph Representation Learning. ICLR Derek Lim, Joshua David Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka
2023	J	`jnl`	Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for Molecule Generation. CoRR Ameya Daigavane, Song Kim, Mario Geiger, Tess E. Smidt
2022	—	`conf`	Deep Learning and Spectral Embedding for Graph Partitioning. PP Alice Gatti, Zhixiong Hu, Tess E. Smidt, Esmond G. Ng, Pieter Ghysels
2022	J	`jnl`	Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs. CoRR Yi-Lun Liao, Tess E. Smidt
2022	A*	`conf`	Generative Coarse-Graining of Molecular Conformations. ICML Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli
2022	J	`jnl`	Generative Coarse-Graining of Molecular Conformations. CoRR Wujie Wang, Minkai Xu, Chen Cai, Benjamin Kurt Miller, Tess E. Smidt, Yusu Wang, Jian Tang, Rafael Gómez-Bombarelli
2022	J	`jnl`	Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks. J. Mach. Learn. Res. Alice Gatti, Zhixiong Hu, Tess E. Smidt, Esmond G. Ng, Pieter Ghysels
2022	J	`jnl`	Learning Integrable Dynamics with Action-Angle Networks. CoRR Ameya Daigavane, Arthur Kosmala, Miles D. Cranmer, Tess E. Smidt, Shirley Ho
2022	J	`jnl`	Sign and Basis Invariant Networks for Spectral Graph Representation Learning. CoRR Derek Lim, Joshua Robinson, Lingxiao Zhao, Tess E. Smidt, Suvrit Sra, Haggai Maron, Stefanie Jegelka
2022	J	`jnl`	e3nn: Euclidean Neural Networks. CoRR Mario Geiger, Tess E. Smidt
2021	J	`jnl`	Deep Learning and Spectral Embedding for Graph Partitioning. CoRR Alice Gatti, Zhixiong Hu, Tess E. Smidt, Esmond G. Ng, Pieter Ghysels
2021	J	`jnl`	Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning. CoRR Alice Gatti, Zhixiong Hu, Pieter Ghysels, Esmond G. Ng, Tess E. Smidt
2021	J	`jnl`	SE(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials. CoRR Simon L. Batzner, Tess E. Smidt, Lixin Sun, Jonathan P. Mailoa, Mordechai Kornbluth, Nicola Molinari, Boris Kozinsky
2021	A*	`conf`	SE(3)-equivariant prediction of molecular wavefunctions and electronic densities. NeurIPS Oliver T. Unke, Mihail Bogojeski, Michael Gastegger, Mario Geiger, Tess E. Smidt, Klaus-Robert Müller
2020	J	`jnl`	Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks. CoRR Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller
2020	J	`jnl`	Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties. CoRR Benjamin Kurt Miller, Mario Geiger, Tess E. Smidt, Frank Noé
2018	J	`jnl`	Tensor Field Networks: Rotation- and Translation-Equivariant Neural Networks for 3D Point Clouds. CoRR Nathaniel Thomas, Tess E. Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, Patrick Riley
2018	—	`—`	Toward the Systematic Design of Complex Materials from Structural Motifs. Tess E. Smidt