David Rolnick

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Priya L. DontiExecutive Director, Climate Change AI; Incoming Assistant Professor, MITVerified email at mit.edu
Lynn H. KaackHertie SchoolVerified email at hertie-school.org
Nikola Milojevic-DupontMercator Research Institute on Global Commons and Climate Change (MCC)Verified email at mcc-berlin.net
Kelly KochanskiPhD, University of Colorado - BoulderVerified email at colorado.edu
Konrad Paul KordingNathan Mossell University Professor (PIK), bioengineering and neuroscience, UPenn, CIFARVerified email at upenn.edu
S. Karthik MukkavilliIBM ResearchVerified email at ibm.com
Evan D. SherwinStanford UniversityVerified email at stanford.edu
Max TegmarkProfessor of Physics, MITVerified email at mit.edu
Sasha LuccioniHugging FaceVerified email at huggingface.co
Serge BelongieUniversity of CopenhagenVerified email at di.ku.dk
Anna Waldman-BrownMIT Industrial Performance CenterVerified email at alum.mit.edu
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Boris HaninAssistant Professor, Princeton ORFEVerified email at princeton.edu
Andrew RossPhD student, Harvard UniversityVerified email at g.harvard.edu
Kris SankaranAssistant Professor of Statistics, University of Wisconsin - MadisonVerified email at wisc.edu
Natasha JaquesGoogle Research, UC BerkeleyVerified email at google.com
Alexandre LacosteStaff Research Scientist, ServiceNow ResearchVerified email at servicenow.com
Tegan MaharajAssistant Professor at University of TorontoVerified email at polymtl.ca
Felix CreutzigMercator Research Institute on Global Commons and Climate Change & TUB, BerlinVerified email at mcc-berlin.net
Andrew NgStanford UniversityVerified email at cs.stanford.edu

David Rolnick

McGill University, Mila Quebec AI Institute

Verified email at cs.mcgill.ca - Homepage

Machine Learning Climate Change Deep Learning Theory


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Why does deep and cheap learning work so well? HW Lin, M Tegmark, D Rolnick Journal of Statistical Physics 168, 1223-1247, 2017	680	2017
Tackling climate change with machine learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... ACM Computing Surveys (CSUR) 55 (2), 1-96, 2022	651*	2022
Deep learning is robust to massive label noise D Rolnick, A Veit, S Belongie, N Shavit arXiv preprint arXiv:1705.10694, 2017	537	2017
Experience replay for continual learning D Rolnick, A Ahuja, J Schwarz, TP Lillicrap, G Wayne Advances in Neural Information Processing Systems, 2019	527	2019
How to start training: The effect of initialization and architecture B Hanin, D Rolnick Advances in Neural Information Processing Systems, 2018	213	2018
The power of deeper networks for expressing natural functions D Rolnick, M Tegmark International Conference on Learning Representations, 2018	191	2018
Complexity of linear regions in deep networks B Hanin, D Rolnick International Conference on Machine Learning, 2596-2604, 2019	160	2019
Deep ReLU networks have surprisingly few activation patterns B Hanin, D Rolnick Advances in Neural Information Processing Systems, 361-370, 2019	158	2019
Measuring and regularizing networks in function space AS Benjamin, D Rolnick, K Kording International Conference on Learning Representations, 2019	99	2019
DC3: A learning method for optimization with hard constraints PL Donti, D Rolnick, JZ Kolter International Conference on Learning Representations, 2021	72	2021
Reverse-engineering deep ReLU networks D Rolnick, K Kording International Conference on Machine Learning, 8178-8187, 2020	66*	2020
Aligning artificial intelligence with climate change mitigation LH Kaack, PL Donti, E Strubell, G Kamiya, F Creutzig, D Rolnick Nature Climate Change 12 (6), 518-527, 2022	42	2022
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	39	2016
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	31	2019
Morphological error detection in 3D segmentations D Rolnick, Y Meirovitch, T Parag, H Pfister, V Jain, JW Lichtman, ... arXiv preprint arXiv:1705.10882, 2017	23	2017
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	17*	2015
Quantitative Tverberg, Helly, & Carathéodory theorems JA De Loera, RN La Haye, D Rolnick, P Soberón arXiv preprint arXiv:1503.06116, 2015	16*	2015
Bugs in the data: How ImageNet misrepresents biodiversity AS Luccioni, D Rolnick AAAI Conference on Artificial Intelligence, 2022	14	2022
Quantitative (p, q) theorems in combinatorial geometry D Rolnick, P Soberón Discrete Mathematics 340 (10), 2516-2527, 2017	14*	2017
Quantitative combinatorial geometry for continuous parameters JA De Loera, RN La Haye, D Rolnick, P Soberón Discrete & Computational Geometry 57, 318-334, 2017	14	2017

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