David Rolnick

Cited by

	All	Since 2016
Citations	1337	1316
h-index	12	11
i10-index	16	16

560

280

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420

201520162017201820192020202112 28 112 207 390 559 8

Co-authors

Konrad Paul KordingUniversity Professor (PIK), bioengineering and neuroscience, UPennVerified email at upenn.edu
Max TegmarkProfessor of Physics, MITVerified email at mit.edu
Alexandra Luccioni (Sasha)Université de Montréal and MilaVerified email at mila.quebec
Pablo SoberónAssistant Professor, Baruch College, CUNYVerified email at baruch.cuny.edu
Boris HaninAssistant Professor, Princeton ORFEVerified email at princeton.edu
Priya L. DontiPh.D. Student, Carnegie Mellon UniversityVerified email at cmu.edu
Lynn H. KaackEnergy Politics Group, ETH ZürichVerified email at gess.ethz.ch
Jesus De LoeraProfessor of Mathematics, University of California, DavisVerified email at math.ucdavis.edu
Henry W LinPrinceton UniversityVerified email at princeton.edu
Natasha JaquesGoogle Research, UC BerkeleyVerified email at google.com
Kelly KochanskiPhD, University of Colorado - BoulderVerified email at colorado.edu
Alexandre LacosteResearch Scientist, Element AIVerified email at elementai.com
Nikola Milojevic-DupontMercator Research Institute on Global Commons and Climate Change (MCC)Verified email at mcc-berlin.net
S. Karthik MukkavilliUS DOE - LBNL & PNNL, Exascale Computing; University of California, Irvine; McGill CIM/CSVerified email at lbl.gov
Anna Waldman-BrownResearch Associate, Work of the Future Task Force, MITVerified email at alum.mit.edu
Evan D. SherwinStanford UniversityVerified email at stanford.edu
Kris SankaranUW MadisonVerified email at wisc.edu
Tegan MaharajPhD student, MILA (Polytechnique Montreal)Verified email at polymtl.ca
Andrew RossPhD student, Harvard UniversityVerified email at g.harvard.edu
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	440	2017
Deep learning is robust to massive label noise D Rolnick, A Veit, S Belongie, N Shavit arXiv preprint arXiv:1705.10694, 2017	231	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	137	2019
How to start training: The effect of initialization and architecture B Hanin, D Rolnick Advances in Neural Information Processing Systems, 571-581, 2018	89	2018
The power of deeper networks for expressing natural functions D Rolnick, M Tegmark arXiv preprint arXiv:1705.05502, 2017	88	2017
Experience replay for continual learning D Rolnick, A Ahuja, J Schwarz, T Lillicrap, G Wayne Advances in Neural Information Processing Systems, 350-360, 2019	82	2019
Complexity of linear regions in deep networks B Hanin, D Rolnick arXiv preprint arXiv:1901.09021, 2019	48	2019
Deep ReLU networks have surprisingly few activation patterns B Hanin, D Rolnick Advances in Neural Information Processing Systems, 361-370, 2019	41	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	23	2016
Quantitative Tverberg, Helly, & Carathéodory theorems JA De Loera, RN La Haye, D Rolnick, P Soberón arXiv preprint arXiv:1503.06116, 2015	16	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
Quantitative (p, q) theorems in combinatorial geometry D Rolnick, P Soberón Discrete Mathematics 340 (10), 2516-2527, 2017	12	2017
Morphological error detection in 3D segmentations D Rolnick, Y Meirovitch, T Parag, H Pfister, V Jain, JW Lichtman, ... arXiv preprint arXiv:1705.10882, 2017	12	2017
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
Measuring and regularizing networks in function space AS Benjamin, D Rolnick, K Kording arXiv preprint arXiv:1805.08289, 2018	10	2018
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
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 Conference on Computer Vision and Pattern …, 2019	9	2019
Generative models and abstractions for large-scale neuroanatomy datasets D Rolnick, EL Dyer Current opinion in neurobiology 55, 112-120, 2019	8	2019
Algorithms for Tverberg's theorem via centerpoint theorems D Rolnick, P Soberón arXiv preprint arXiv:1601.03083, 2016	8*	2016
Acyclic subgraphs of planar digraphs N Golowich, D Rolnick arXiv preprint arXiv:1407.8045, 2014	7	2014

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