Risi Kondor

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Gabor CsanyiProfessor of Molecular Modelling, Engineering Laboratory, University of CambridgeEmail confirmado em cam.ac.uk
Shubhendu TrivediMassachusetts Institute of TechnologyEmail confirmado em csail.mit.edu
Albert P. BartokUniversity of WarwickEmail confirmado em warwick.ac.uk
Tony JebaraSpotifyEmail confirmado em spotify.com
Brandon M AndersonAtomwise, Inc.Email confirmado em jfi.uchicago.edu
Horace PanUniversity of ChicagoEmail confirmado em uchicago.edu
Karsten BorgwardtDirector, Max Planck Institute of BiochemistryEmail confirmado em biochem.mpg.de
Vikas SinghUniversity of Wisconsin MadisonEmail confirmado em biostat.wisc.edu
Andrew HowardGoogle ResearchEmail confirmado em google.com
S V N VishwanathanAssociate Professor of Statistics and Computer Science, Purdue UniversityEmail confirmado em stat.purdue.edu
Deepti PachauriResearch ScientistEmail confirmado em cs.wisc.edu
Alex SmolaBoson AIEmail confirmado em smola.org
Erik ThiedeAsst. Prof of Chemistry and Chemical Biology, Cornell UniversityEmail confirmado em cornell.edu
Alexander BogatskiyFlatiron InstituteEmail confirmado em flatironinstitute.org
Nedelina TenevaUniversity of ChicagoEmail confirmado em uchicago.edu
Pramod Kaushik MudrakartaGoogle ResearchEmail confirmado em google.com
Wenda ZhouNew York UniversityEmail confirmado em nyu.edu
Zhen LinUniversity of Illinois at Urbana-ChampaignEmail confirmado em illinois.edu
Vikas GargMassachusetts Institute of Technology (MIT)Email confirmado em mit.edu
Jean-Philippe VertOwkin / PSL UniversityEmail confirmado em owkin.com

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Risi Kondor

Associate Professor, The University of Chicago

Email confirmado em cs.uchicago.edu - Página inicial

Machine Learning Computational Harmonic Analysis Deep Learning Machine Learning for Physics


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	2893	2010
On representing chemical environments AP Bartók, R Kondor, G Csányi Physical Review B—Condensed Matter and Materials Physics 87 (18), 184115, 2013	2536	2013
Diffusion kernels on graphs and other discrete structures RI Kondor, J Lafferty Proceedings of the 19th international conference on machine learning 2002 …, 2002	1732	2002
Graph kernels SVN Vishwanathan, NN Schraudolph, R Kondor, KM Borgwardt The Journal of Machine Learning Research 11, 1201-1242, 2010	1408	2010
Kernels and regularization on graphs AJ Smola, R Kondor Learning Theory and Kernel Machines: 16th Annual Conference on Learning …, 2003	1136	2003
Probability product kernels T Jebara, R Kondor, A Howard The Journal of Machine Learning Research 5, 819-844, 2004	686	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	559	2018
Cormorant: Covariant molecular neural networks B Anderson, TS Hy, R Kondor Advances in neural information processing systems 32, 2019	503	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	304	2018
The multiscale laplacian graph kernel R Kondor, H Pan Advances in neural information processing systems 29, 2016	216	2016
Solving the multi-way matching problem by permutation synchronization D Pachauri, R Kondor, V Singh Advances in neural information processing systems 26, 2013	212	2013
Bhattacharyya and expected likelihood kernels T Jebara, R Kondor Learning Theory and Kernel Machines: 16th Annual Conference on Learning …, 2003	197	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	181	2020
Group theoretical methods in machine learning IR Kondor Columbia University, 2008	153	2008
Covariant compositional networks for learning graphs R Kondor, HT Son, H Pan, B Anderson, S Trivedi arXiv preprint arXiv:1801.02144, 2018	152	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	130	2020
N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials R Kondor arXiv preprint arXiv:1803.01588, 2018	122	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	98	2018

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