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teaching:ws2025:dl-equi-dynam:papers [2025/09/26 14:09] (current)
Karnik Ram created |
| | ^No. ^Paper ^ |
| | | 1 | [[https://arxiv.org/abs/1807.02547 |3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data]]| |
| | | 2 | [[https://www.nature.com/articles/s41467-022-29939-5 | E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials |
| | ]]| |
| | | 3 | [[https://arxiv.org/abs/2206.07697| MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields |
| | ]] | |
| | | 4 | [[https://arxiv.org/abs/1802.08219| Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds]] | |
| | | 5 | [[https://arxiv.org/abs/2506.13523|The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in |
| | Equivariant Tensor Products]] | |
| | | 6 | [[https://arxiv.org/abs/2302.03655|Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs]]| |
| | | 7 | [[https://arxiv.org/abs/2305.18415 | Geometric Algebra Transformers]] | |
| | | 8 | [[https://arxiv.org/abs/2310.01647 | Equivariant Adaptation of Large Pretrained Models |
| | ]] | |
| | | 9 | [[https://arxiv.org/abs/2110.03336 | Frame Averaging for Invariant and Equivariant Network Design]] | |
| | | 10 | [[https://arxiv.org/abs/2405.19296 | Neural Isometries: Taming Transformations for Equivariant ML |
| | ]] | |
| | | 11 | [[https://arxiv.org/abs/2507.14793 | Flow Equivariant Recurrent Neural Networks |
| | ]] | |
| | |12 | [[https://arxiv.org/abs/2501.01999|Probing Equivariance and Symmetry Breaking in Convolutional Networks |
| | ]]| |
| | | 13 |[[https://arxiv.org/abs/2506.18340|Controlled Generation with Equivariant Variational Flow Matching |
| | ]]| |
| | | 14 | [[https://arxiv.org/abs/1806.07366| Neural Ordinary Differential Equations]]| |
| | | 15 | [[https://arxiv.org/abs/2410.13821|Artificial Kuramoto Oscillatory Neurons]]| |
| | | 16 | [[https://arxiv.org/abs/2310.02391|SE(3)-Stochastic Flow Matching for Protein Backbone Generation]]| |
| | | 17 | [[https://arxiv.org/abs/2506.17139|Consistent Sampling and Simulation: Molecular |
| | Dynamics with Energy-Based Diffusion Models]] | |
| | | 18| [[https://arxiv.org/abs/2405.03987 | Navigating Chemical Space with Latent Flows |
| | ]]| |
| | | 19 | [[https://arxiv.org/abs/2210.06662|Action Matching: Learning Stochastic Dynamics from Samples |
| | ]] | |
| | | 20 | [[https://arxiv.org/abs/2504.18506|Action-Minimization Meets Generative Modeling: Efficient Transition Path Sampling with the Onsager-Machlup Functional |
| | ]] | |
| | | 21 | [[https://arxiv.org/abs/2410.07974|Doob's Lagrangian: A Sample-Efficient Variational Approach to Transition Path Sampling |
| | ]] | |
| | | 22 | [[https://arxiv.org/abs/2504.11516|FEAT: Free energy Estimators with Adaptive Transport |
| | ]] | |
| | | 23 | [[https://arxiv.org/abs/2209.14855|Continuous PDE Dynamics Forecasting with Implicit Neural Representations]] | |
| | | 24 | [[https://arxiv.org/abs/2406.06660|Space-Time Continuous PDE Forecasting using |
| | Equivariant Neural Fields]] | |
| | | 25 | [[https://arxiv.org/abs/2507.11531|Langevin Flows for Modeling Neural Latent Dynamics]] | |
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