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Tegan Maharaj
Tegan Maharaj
Assistant Professor at University of Toronto
Zweryfikowany adres z polymtl.ca - Strona główna
Tytuł
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Cytowane przez
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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
9622017
Tackling climate change with machine learning
D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ...
ACM Computing Surveys (CSUR) 55 (2), 1-96, 2022
3712022
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
3062016
Extremeweather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
E Racah, C Beckham, T Maharaj, S Ebrahimi Kahou, M Prabhat, C Pal
Advances in neural information processing systems 30, 2017
1742017
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
1452020
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
682017
Deep nets don't learn via memorization
D Krueger, N Ballas, S Jastrzebski, D Arpit, MS Kanwal, T Maharaj, ...
562017
Tackling climate change with machine learning (2019)
D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ...
arXiv preprint arxiv:1906.05433, 1906
311906
Covi white paper
H Alsdurf, E Belliveau, Y Bengio, T Deleu, P Gupta, D Ippolito, R Janda, ...
arXiv preprint arXiv:2005.08502, 2020
282020
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
162017
Hidden incentives for auto-induced distributional shift
D Krueger, T Maharaj, J Leike
arXiv preprint arXiv:2009.09153, 2020
122020
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
82020
Surprisal-driven zoneout
K Rocki, T Kornuta, T Maharaj
arXiv preprint arXiv:1610.07675, 2016
82016
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
52017
Filling gaps in trustworthy development of AI
S Avin, H Belfield, M Brundage, G Krueger, J Wang, A Weller, ...
Science 374 (6573), 1327-1329, 2021
42021
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
32020
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
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
Deep Learning for Detecting Extreme Weather Patterns
M Mudigonda, P Ram, K Kashinath, E Racah, A Mahesh, Y Liu, ...
Deep Learning for the Earth Sciences: A Comprehensive Approach to Remote …, 2021
12021
Memorization in recurrent neural networks
T Maharaj, D Krueger, T Coojimans
Workshop on Principled Approaches to Deep Learning, 2017
12017
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Prace 1–20