David Rolnick

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Priya L. DontiPh.D. Student, Carnegie Mellon UniversityVerified email at cmu.edu
Konrad Paul KordingUniversity Professor (PIK), bioengineering and neuroscience, UPennVerified email at upenn.edu
Lynn H. KaackEnergy Politics Group, ETH ZürichVerified email at gess.ethz.ch
Alexandra [Sasha] LuccioniUniversité de Montréal and MilaVerified email at mila.quebec
Boris HaninAssistant Professor, Princeton ORFEVerified email at princeton.edu
Max TegmarkProfessor of Physics, MITVerified email at mit.edu
Nikola Milojevic-DupontMercator Research Institute on Global Commons and Climate Change (MCC)Verified email at mcc-berlin.net
Anna Waldman-BrownResearch Associate, Work of the Future Task Force, MITVerified email at alum.mit.edu
Kelly KochanskiPhD, University of Colorado - BoulderVerified email at colorado.edu
S. Karthik MukkavilliUS DOE - LBNL & PNNL, Exascale Computing; University of California, Irvine; McGill CIM/CSVerified email at lbl.gov
Evan D. SherwinStanford UniversityVerified email at stanford.edu
Kris SankaranUW MadisonVerified email at wisc.edu
Andrew RossPhD student, Harvard UniversityVerified email at g.harvard.edu
Pablo SoberónAssistant Professor, Baruch College, CUNYVerified email at baruch.cuny.edu
Henry W LinPrinceton UniversityVerified email at princeton.edu
Jesus De LoeraProfessor of Mathematics, University of California, DavisVerified email at math.ucdavis.edu
Natasha JaquesGoogle Research, UC BerkeleyVerified email at google.com
Alexandre LacosteResearch Scientist, Element AIVerified email at elementai.com
Tegan MaharajPhD student, MILA (Polytechnique Montreal)Verified email at polymtl.ca
Serge BelongieProfessor of Computer Science, Cornell University and Cornell TechVerified email at cs.cornell.edu

David Rolnick

McGill University, Mila Quebec AI Institute

Verified email at cs.mcgill.ca - Homepage

Machine Learning Deep Learning Theory Climate Change


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Why does deep and cheap learning work so well? HW Lin, M Tegmark, D Rolnick Journal of Statistical Physics 168 (6), 1223-1247, 2017	483	2017
Deep learning is robust to massive label noise D Rolnick, A Veit, S Belongie, N Shavit arXiv preprint arXiv:1705.10694, 2017	260	2017
Tackling climate change with machine learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... arXiv preprint arXiv:1906.05433, 2019	187	2019
The power of deeper networks for expressing natural functions D Rolnick, M Tegmark International Conference on Learning Representations, 2018	105	2018
Experience replay for continual learning D Rolnick, A Ahuja, J Schwarz, TP Lillicrap, G Wayne Advances in Neural Information Processing Systems, 2019	96	2019
How to start training: The effect of initialization and architecture B Hanin, D Rolnick Advances in Neural Information Processing Systems, 2018	96	2018
Complexity of linear regions in deep networks B Hanin, D Rolnick International Conference on Machine Learning, 2596-2604, 2019	64	2019
Deep ReLU networks have surprisingly few activation patterns B Hanin, D Rolnick Advances in Neural Information Processing Systems, 361-370, 2019	52	2019
A multi-pass approach to large-scale connectomics Y Meirovitch, A Matveev, H Saribekyan, D Budden, D Rolnick, G Odor, ... arXiv preprint arXiv:1612.02120, 2016	26	2016
Quantitative Tverberg, Helly, & Carathéodory theorems JA De Loera, RN La Haye, D Rolnick, P Soberón arXiv preprint arXiv:1503.06116, 2015	15	2015
Graph-coloring ideals: Nullstellensatz certificates, Gröbner bases for chordal graphs, and hardness of Gröbner bases JA De Loera, S Margulies, M Pernpeintner, E Riedl, D Rolnick, G Spencer, ... Proceedings of the 2015 ACM on International Symposium on Symbolic and …, 2015	14*	2015
Reverse-engineering deep ReLU networks D Rolnick, K Kording International Conference on Machine Learning, 8178-8187, 2020	13*	2020
Morphological error detection in 3D segmentations D Rolnick, Y Meirovitch, T Parag, H Pfister, V Jain, JW Lichtman, ... arXiv preprint arXiv:1705.10882, 2017	13	2017
Quantitative (p, q) theorems in combinatorial geometry D Rolnick, P Soberón Discrete Mathematics 340 (10), 2516-2527, 2017	12*	2017
Measuring and regularizing networks in function space AS Benjamin, D Rolnick, K Kording International Conference on Learning Representations, 2019	11	2019
Quantitative Tverberg theorems over lattices and other discrete sets JA De Loera, RN La Haye, D Rolnick, P Soberón Discrete & Computational Geometry 58 (2), 435-448, 2017	11	2017
Cross-classification clustering: An efficient multi-object tracking technique for 3-D instance segmentation in connectomics Y Meirovitch, L Mi, H Saribekyan, A Matveev, D Rolnick, N Shavit Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	10	2019
Quantitative combinatorial geometry for continuous parameters JA De Loera, RN La Haye, D Rolnick, P Soberón Discrete & Computational Geometry 57 (2), 318-334, 2017	10	2017
Generative models and abstractions for large-scale neuroanatomy datasets D Rolnick, EL Dyer Current opinion in neurobiology 55, 112-120, 2019	9	2019
Algorithms for Tverberg's theorem via centerpoint theorems D Rolnick, P Soberón arXiv preprint arXiv:1601.03083, 2016	8*	2016

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