Relevance of Rotationally Equivariant Convolutions for Predicting
Molecular Properties

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties

19 August 2020

Benjamin Kurt Miller

Frank Noé

Papers citing "Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties"

18 / 18 papers shown

Title
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching Aaron J. Havens Benjamin Kurt Miller Bing Yan Carles Domingo-Enrich Anuroop Sriram ... Brandon Amos Brian Karrer Xiang Fu Guan-Horng Liu Ricky T. Q. Chen DiffM 73 1 0 16 Apr 2025
Permutation Equivariant Neural Networks for Symmetric Tensors Edward Pearce-Crump 106 1 0 14 Mar 2025
Molecule Graph Networks with Many-body Equivariant Interactions Zetian Mao Jiawen Li Chen Liang Diptesh Das Masato Sumita Koji Tsuda Kelin Xia Koji Tsuda 56 1 0 19 Jun 2024
Coarse Graining Molecular Dynamics with Graph Neural Networks B. Husic N. Charron Dominik Lemm Jiang Wang Adria Pérez ... Yaoyi Chen Simon Olsson Gianni De Fabritiis Frank Noé C. Clementi AI4CE 58 159 0 22 Jul 2020
Geometric Prediction: Moving Beyond Scalars Raphael J. L. Townshend Brent Townshend Stephan Eismann R. Dror 26 7 0 25 Jun 2020
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach Jiang Wang Stefan Chmiela K. Müller Frank Noè C. Clementi 100 46 0 04 May 2020
Directional Message Passing for Molecular Graphs Johannes Klicpera Janek Groß Stephan Günnemann 93 861 0 06 Mar 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 128 42,038 0 03 Dec 2019
Cormorant: Covariant Molecular Neural Networks Brandon M. Anderson Truong-Son Hy Risi Kondor 72 423 0 06 Jun 2019
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data Maurice Weiler Mario Geiger Max Welling Wouter Boomsma Taco S. Cohen 3DPC 58 501 0 06 Jul 2018
Relational inductive biases, deep learning, and graph networks Peter W. Battaglia Jessica B. Hamrick V. Bapst Alvaro Sanchez-Gonzalez V. Zambaldi ... Pushmeet Kohli M. Botvinick Oriol Vinyals Yujia Li Razvan Pascanu AI4CE NAI 356 3,101 0 04 Jun 2018
Revisiting Small Batch Training for Deep Neural Networks Dominic Masters Carlo Luschi ODL 53 665 0 20 Apr 2018
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds Nathaniel Thomas Tess E. Smidt S. Kearnes Lusann Yang Li Li Kai Kohlhoff Patrick F. Riley 3DPC 69 959 0 22 Feb 2018
Don't Decay the Learning Rate, Increase the Batch Size Samuel L. Smith Pieter-Jan Kindermans Chris Ying Quoc V. Le ODL 82 990 0 01 Nov 2017
Train longer, generalize better: closing the generalization gap in large batch training of neural networks Elad Hoffer Itay Hubara Daniel Soudry ODL 134 798 0 24 May 2017
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 288 1,808 0 02 Mar 2017
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 585 149,474 0 22 Dec 2014
Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning M. Rupp A. Tkatchenko K. Müller O. A. von Lilienfeld AI4CE 98 1,581 0 12 Sep 2011