David Rolnick
David Rolnick
McGill University, Mila Quebec AI Institute
Verified email at cs.mcgill.ca - Homepage
Cited by
Cited by
Why does deep and cheap learning work so well?
HW Lin, M Tegmark, D Rolnick
Journal of Statistical Physics 168 (6), 1223-1247, 2017
Deep learning is robust to massive label noise
D Rolnick, A Veit, S Belongie, N Shavit
arXiv preprint arXiv:1705.10694, 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
The power of deeper networks for expressing natural functions
D Rolnick, M Tegmark
arXiv preprint arXiv:1705.05502, 2017
How to start training: The effect of initialization and architecture
B Hanin, D Rolnick
arXiv preprint arXiv:1803.01719, 2018
Experience replay for continual learning
D Rolnick, A Ahuja, J Schwarz, TP Lillicrap, G Wayne
arXiv preprint arXiv:1811.11682, 2018
Complexity of linear regions in deep networks
B Hanin, D Rolnick
International Conference on Machine Learning, 2596-2604, 2019
Deep ReLU networks have surprisingly few activation patterns
B Hanin, D Rolnick
Advances in Neural Information Processing Systems, 361-370, 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
Quantitative Tverberg, Helly, & Carathéodory theorems
JA De Loera, RN La Haye, D Rolnick, P Soberón
arXiv preprint arXiv:1503.06116, 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
Morphological error detection in 3D segmentations
D Rolnick, Y Meirovitch, T Parag, H Pfister, V Jain, JW Lichtman, ...
arXiv preprint arXiv:1705.10882, 2017
Quantitative (p, q) theorems in combinatorial geometry
D Rolnick, P Soberón
Discrete Mathematics 340 (10), 2516-2527, 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
Measuring and regularizing networks in function space
AS Benjamin, D Rolnick, K Kording
arXiv preprint arXiv:1805.08289, 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
Generative models and abstractions for large-scale neuroanatomy datasets
D Rolnick, EL Dyer
Current opinion in neurobiology 55, 112-120, 2019
Reverse-Engineering Deep ReLU Networks
D Rolnick, KP Körding
CoRR abs/1910.00744, 2019
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
Algorithms for Tverberg's theorem via centerpoint theorems
D Rolnick, P Soberón
arXiv preprint arXiv:1601.03083, 2016
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