Tegan Maharaj
Tegan Maharaj
PhD student, MILA (Polytechnique Montreal)
Verified email at polymtl.ca - Homepage
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, ...
International Conference on Machine Learning, 233-242, 2017
7132017
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
2712016
Tackling climate change with machine learning
D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ...
arXiv preprint arXiv:1906.05433, 2019
243*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
1282016
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
852020
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
552017
Deep nets don't learn via memorization
D Krueger, N Ballas, S Jastrzebski, D Arpit, MS Kanwal, T Maharaj, ...
502017
Covi white paper
H Alsdurf, E Belliveau, Y Bengio, T Deleu, P Gupta, D Ippolito, R Janda, ...
arXiv preprint arXiv:2005.08502, 2020
262020
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) 5, 2017
152017
Semi-supervised detection of extreme weather events in large climate datasets
E Racah, C Beckham, T Maharaj, C Pal
152016
Surprisal-driven zoneout
K Rocki, T Kornuta, T Maharaj
arXiv preprint arXiv:1610.07675, 2016
92016
Hidden Incentives for Auto-Induced Distributional Shift
D Krueger, T Maharaj, J Leike
arXiv preprint arXiv:2009.09153, 2020
72020
Misleading meta-objectives and hidden incentives for distributional shift
D Krueger, T Maharaj, S Legg, J Leike
Safe Machine Learning workshop at ICLR, 2019
32019
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
32017
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
32017
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
22020
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
12020
Memorization in Recurrent Neural Networks
T Maharaj, D Krueger, T Coojimans
Workshop on Principled Approaches to Deep Learning, 2017
12017
Deep Learning for Detecting Extreme Weather Patterns
M Mudigonda, Prabhat Ram, K Kashinath, E Racah, A Mahesh, Y Liu, ...
Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021
2021
Hidden incentives for self-induced distributional shift
DS Krueger, T Maharaj, S Legg, J Leike
2019
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Articles 1–20