フォロー
Tegan Maharaj
Tegan Maharaj
Assistant Professor at University of Toronto
確認したメール アドレス: polymtl.ca - ホームページ
タイトル
引用先
引用先
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
19302017
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
10212022
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
3802016
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
3512020
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
2772017
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
1122017
Deep nets don't learn via memorization
D Krueger, N Ballas, S Jastrzebski, D Arpit, MS Kanwal, T Maharaj, ...
722017
Managing AI risks in an era of rapid progress
Y Bengio, G Hinton, A Yao, D Song, P Abbeel, YN Harari, YQ Zhang, ...
arXiv preprint arXiv:2310.17688, 2023
532023
Hidden incentives for auto-induced distributional shift
D Krueger, T Maharaj, J Leike
arXiv preprint arXiv:2009.09153, 2020
532020
Tackling climate change with machine learning (2019)
D Rolnick, PL Donti, LH Kaack, K Kochanski, A Lacoste, K Sankaran, ...
Preprint available at, 1906
461906
Covi white paper
H Alsdurf, E Belliveau, Y Bengio, T Deleu, P Gupta, D Ippolito, R Janda, ...
arXiv preprint arXiv:2005.08502, 2020
452020
Harms from increasingly agentic algorithmic systems
A Chan, R Salganik, A Markelius, C Pang, N Rajkumar, D Krasheninnikov, ...
Proceedings of the 2023 ACM Conference on Fairness, Accountability, and …, 2023
412023
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
362021
Foundational challenges in assuring alignment and safety of large language models
U Anwar, A Saparov, J Rando, D Paleka, M Turpin, P Hase, ES Lubana, ...
arXiv preprint arXiv:2404.09932, 2024
292024
Metadata archaeology: Unearthing data subsets by leveraging training dynamics
SA Siddiqui, N Rajkumar, T Maharaj, D Krueger, S Hooker
arXiv preprint arXiv:2209.10015, 2022
232022
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
212020
Managing extreme AI risks amid rapid progress
Y Bengio, G Hinton, A Yao, D Song, P Abbeel, T Darrell, YN Harari, ...
Science 384 (6698), 842-845, 2024
152024
Surprisal-driven zoneout
K Rocki, T Kornuta, T Maharaj
arXiv preprint arXiv:1610.07675, 2016
122016
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
112020
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
72017
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