| Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons AP Bartók, MC Payne, R Kondor, G Csányi Physical review letters 104 (13), 136403, 2010 | 2961 | 2010 |
| On representing chemical environments AP Bartók, R Kondor, G Csányi Physical Review B—Condensed Matter and Materials Physics 87 (18), 184115, 2013 | 2613 | 2013 |
| Diffusion kernels on graphs and other discrete structures RI Kondor, J Lafferty Proceedings of the 19th international conference on machine learning 2002 …, 2002 | 1730 | 2002 |
| Graph kernels SVN Vishwanathan, NN Schraudolph, R Kondor, KM Borgwardt The Journal of Machine Learning Research 11, 1201-1242, 2010 | 1429 | 2010 |
| Kernels and regularization on graphs AJ Smola, R Kondor Learning Theory and Kernel Machines: 16th Annual Conference on Learning …, 2003 | 1141 | 2003 |
| Probability product kernels T Jebara, R Kondor, A Howard Journal of Machine Learning Research 5 (Jul), 819-844, 2004 | 687 | 2004 |
| On the generalization of equivariance and convolution in neural networks to the action of compact groups R Kondor, S Trivedi International conference on machine learning, 2747-2755, 2018 | 578 | 2018 |
| Cormorant: Covariant molecular neural networks B Anderson, TS Hy, R Kondor Advances in neural information processing systems 32, 2019 | 525 | 2019 |
| A kernel between sets of vectors R Kondor, T Jebara Proceedings of the 20th international conference on machine learning (ICML …, 2003 | 363 | 2003 |
| Clebsch–gordan nets: a fully fourier space spherical convolutional neural network R Kondor, Z Lin, S Trivedi Advances in Neural Information Processing Systems 31, 2018 | 316 | 2018 |
| The multiscale laplacian graph kernel R Kondor, H Pan Advances in neural information processing systems 29, 2016 | 218 | 2016 |
| Solving the multi-way matching problem by permutation synchronization D Pachauri, R Kondor, V Singh Advances in neural information processing systems 26, 2013 | 218 | 2013 |
| Bhattacharyya and expected likelihood kernels T Jebara, R Kondor Learning Theory and Kernel Machines: 16th Annual Conference on Learning …, 2003 | 202 | 2003 |
| Lorentz group equivariant neural network for particle physics A Bogatskiy, B Anderson, J Offermann, M Roussi, D Miller, R Kondor International Conference on Machine Learning, 992-1002, 2020 | 182 | 2020 |
| Group theoretical methods in machine learning IR Kondor Columbia University, 2008 | 154 | 2008 |
| Covariant compositional networks for learning graphs R Kondor, HT Son, H Pan, B Anderson, S Trivedi arXiv preprint arXiv:1801.02144, 2018 | 150 | 2018 |
| Atom3d: Tasks on molecules in three dimensions RJL Townshend, M Vögele, P Suriana, A Derry, A Powers, Y Laloudakis, ... arXiv preprint arXiv:2012.04035, 2020 | 137 | 2020 |
| N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials R Kondor arXiv preprint arXiv:1803.01588, 2018 | 130 | 2018 |
| Multi-object tracking with representations of the symmetric group R Kondor, A Howard, T Jebara Artificial intelligence and statistics, 211-218, 2007 | 103 | 2007 |
| Predicting molecular properties with covariant compositional networks TS Hy, S Trivedi, H Pan, BM Anderson, R Kondor The Journal of chemical physics 148 (24), 2018 | 96 | 2018 |
Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of Cambridge在 cam.ac.uk 的电子邮件经过验证