| A closer look at memorization in deep networks D Arpit, S Jastrzębski, N Ballas, D Krueger, E Bengio, MS Kanwal, ... arXiv preprint arXiv:1706.05394, 2017 | 379 | 2017 |
| Zoneout: Regularizing rnns by randomly preserving hidden activations D Krueger, T Maharaj, J Kramár, M Pezeshki, N Ballas, NR Ke, A Goyal, ... arXiv preprint arXiv:1606.01305, 2016 | 215 | 2016 |
| Tackling climate change with machine learning D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ... arXiv preprint arXiv:1906.05433, 2019 | 93 | 2019 |
| ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events E Racah, C Beckham, T Maharaj, SE Kahou, M Prabhat, C Pal Advances in Neural Information Processing Systems, 3402-3413, 2017 | 67 | 2017 |
| A dataset and exploration of models for understanding video data through fill-in-the-blank question-answering T Maharaj, N Ballas, A Rohrbach, A Courville, C Pal Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 38 | 2017 |
| Deep nets don't learn via memorization D Krueger, N Ballas, S Jastrzebski, D Arpit, MS Kanwal, T Maharaj, ... | 33 | 2017 |
| Semi-supervised detection of extreme weather events in large climate datasets E Racah, C Beckham, T Maharaj, C Pal arXiv preprint arXiv:1612.02095, 2016 | 15 | 2016 |
| Toward trustworthy AI development: mechanisms for supporting verifiable claims M Brundage, S Avin, J Wang, H Belfield, G Krueger, G Hadfield, H Khlaaf, ... arXiv preprint arXiv:2004.07213, 2020 | 13 | 2020 |
| A closer look at memorization in deep networks D Krueger, N Ballas, S Jastrzebski, D Arpit, MS Kanwal, T Maharaj, ... International Conference on Machine Learning (ICML), 2017 | 11 | 2017 |
| Surprisal-driven zoneout K Rocki, T Kornuta, T Maharaj arXiv preprint arXiv:1610.07675, 2016 | 8 | 2016 |
| COVI White Paper H Alsdurf, Y Bengio, T Deleu, P Gupta, D Ippolito, R Janda, M Jarvie, ... arXiv preprint arXiv:2005.08502, 2020 | 7 | 2020 |
| Prabhat, and C. Pal, 2017: ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events. 31st Conf. on … E Racah, C Beckham, T Maharaj, S Kahou CA, Neural Information Processing Systems, https://papers. nips. cc/paper …, 0 | 7 | |
| Deep Learning for Extreme Weather Detection M Prabhat, E Racah, J Biard, Y Liu, M Mudigonda, K Kashinath, ... AGUFM 2017, IN11A-0022, 2017 | 3 | 2017 |
| Misleading metaobjectives and hidden incentives for distributional shift D Krueger, T Maharaj, S Legg, J Leike Safe Machine Learning workshop at ICLR, 2019 | 1 | 2019 |
| Climatenet: A machine learning dataset for climate science research M Prabhat, J Biard, S Ganguly, S Ames, K Kashinath, SK Kim, S Kahou, ... AGUFM 2017, IN13E-01, 2017 | 1 | 2017 |
| Hidden incentives for self-induced distributional shift DS Krueger, T Maharaj, S Legg, J Leike | | 2019 |
| Deep Learning recognizes weather and climate patterns K Kashinath, M Prabhat, M Mudigonda, A Mahesh, SK Kim, Y Liu, ... AGUFM 2018, IN14A-07, 2018 | | 2018 |
| Reserve Output Units for Deep Open-Set Learning T Maharaj, D Krueger | | 2018 |
| Practical Applications of Biological Realism in Artificial Neural Networks T Maharaj Bishop's University, 2015 | | 2015 |
| Investigating interpolation and extrapolation in learned representations of ecological network dynamics T Maharaj, C Pal 2020 ESA Annual Meeting (August 3-6), 0 | | |
David Scott KruegerPhD Student, University of MontrealVerified email at umontreal.ca
