| A closer look at memorization in deep networks D Arpit, S Jastrzębski, N Ballas, D Krueger, E Bengio, MS Kanwal, ... International Conference on Machine Learning, 233-242, 2017 | 481 | 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 | 235 | 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 | 138 | 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, C Pal arXiv preprint arXiv:1612.02095, 2016 | 89 | 2016 |
| 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 | 42 | 2017 |
| Deep nets don't learn via memorization D Krueger, N Ballas, S Jastrzebski, D Arpit, MS Kanwal, T Maharaj, ... | 40 | 2017 |
| 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 | 31 | 2020 |
| 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 | 16 | 2016 |
| Covi white paper H Alsdurf, Y Bengio, T Deleu, P Gupta, D Ippolito, R Janda, M Jarvie, ... arXiv preprint arXiv:2005.08502, 2020 | 14 | 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 | 12 | 2017 |
| Surprisal-driven zoneout K Rocki, T Kornuta, T Maharaj arXiv preprint arXiv:1610.07675, 2016 | 9 | 2016 |
| Misleading metaobjectives and hidden incentives for distributional shift D Krueger, T Maharaj, S Legg, J Leike Safe Machine Learning workshop at ICLR, 2019 | 2 | 2019 |
| Deep Learning for Extreme Weather Detection M Prabhat, E Racah, J Biard, Y Liu, M Mudigonda, K Kashinath, ... AGU Fall Meeting Abstracts 2017, IN11A-0022, 2017 | 2 | 2017 |
| ClimateNet: a machine learning dataset for climate science research M Prabhat, J Biard, S Ganguly, S Ames, K Kashinath, SK Kim, S Kahou, ... AGU Fall Meeting Abstracts 2017, IN13E-01, 2017 | 1 | 2017 |
| COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing P Gupta, T Maharaj, M Weiss, N Rahaman, H Alsdurf, A Sharma, ... arXiv preprint arXiv:2010.16004, 2020 | | 2020 |
| Predicting Infectiousness for Proactive Contact Tracing Y Bengio, P Gupta, T Maharaj, N Rahaman, M Weiss, T Deleu, E Muller, ... arXiv preprint arXiv:2010.12536, 2020 | | 2020 |
| Hidden Incentives for Auto-Induced Distributional Shift D Krueger, T Maharaj, J Leike arXiv preprint arXiv:2009.09153, 2020 | | 2020 |
| 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, ... AGU Fall Meeting Abstracts 2018, IN14A-07, 2018 | | 2018 |
| Reserve Output Units for Deep Open-Set Learning T Maharaj, D Krueger | | 2018 |
David Scott KruegerPhD Student, University of MontrealZweryfikowany adres z umontreal.ca
