Tegan Maharaj
Title
Cited by
Cited by
Year
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
3992017
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
2192016
Tackling climate change with machine learning
D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ...
arXiv preprint arXiv:1906.05433, 2019
1052019
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
702017
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
402017
Deep nets don't learn via memorization
D Krueger, N Ballas, S Jastrzebski, D Arpit, MS Kanwal, T Maharaj, ...
342017
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
152020
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
152016
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
112017
COVI White Paper
H Alsdurf, Y Bengio, T Deleu, P Gupta, D Ippolito, R Janda, M Jarvie, ...
arXiv preprint arXiv:2005.08502, 2020
102020
Surprisal-driven zoneout
K Rocki, T Kornuta, T Maharaj
arXiv preprint arXiv:1610.07675, 2016
82016
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
32017
Misleading metaobjectives and hidden incentives for distributional shift
D Krueger, T Maharaj, S Legg, J Leike
Safe Machine Learning workshop at ICLR, 2019
12019
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
12017
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, ...
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
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Articles 1–20