关注
Dale Schuurmans
Dale Schuurmans
University of Alberta, Google DeepMind
在 cs.ualberta.ca 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Chain of thought prompting elicits reasoning in large language models
J Wei, X Wang, D Schuurmans, M Bosma, E Chi, Q Le, D Zhou
Advances in Neural Information Processing Systems 35, 2022
6546*2022
Self-consistency improves chain of thought reasoning in language models
X Wang, J Wei, D Schuurmans, Q Le, E Chi, S Narang, A Chowdhery, ...
arXiv preprint arXiv:2203.11171, 2022
1557*2022
Least-to-most prompting enables complex reasoning in large language models
D Zhou, N Schärli, L Hou, J Wei, N Scales, X Wang, D Schuurmans, C Cui, ...
arXiv preprint arXiv:2205.10625, 2022
7982022
An optimistic perspective on offline reinforcement learning
R Agarwal, D Schuurmans, M Norouzi
International conference on machine learning, 104-114, 2020
630*2020
Maximum margin clustering
L Xu, J Neufeld, B Larson, D Schuurmans
Advances in neural information processing systems 17, 2004
6302004
Bridging the gap between value and policy based reinforcement learning
O Nachum, M Norouzi, K Xu, D Schuurmans
Advances in neural information processing systems 30, 2017
5082017
Advances in Large-Margin Classifiers
PJ Bartlett, B Schölkopf, D Schuurmans, AJ Smola
MIT Press 155, 156, 2000
486*2000
Learning with a Strong Adversary
R Huang, B Xu, D Schuurmans, C Szepesvari
https://arxiv.org/abs/1511.03034, 2015
4482015
Automatic Gait Optimization With Gaussian Process Regression.
DJ Lizotte, T Wang, MH Bowling, D Schuurmans
IJCAI 7, 944-949, 2007
4022007
Augmenting naive bayes classifiers with statistical language models
F Peng, D Schuurmans, S Wang
Information Retrieval 7, 317-345, 2004
3882004
Boosting in the limit: Maximizing the margin of learned ensembles
AJ Grove, D Schuurmans
AAAI/IAAI, 692-699, 1998
3821998
Discriminative batch mode active learning
Y Guo, D Schuurmans
Advances in neural information processing systems 20, 2007
3792007
What learning algorithm is in-context learning? investigations with linear models
E Akyürek, D Schuurmans, J Andreas, T Ma, D Zhou
arXiv preprint arXiv:2211.15661, 2022
3062022
On the global convergence rates of softmax policy gradient methods
J Mei, C Xiao, C Szepesvari, D Schuurmans
International conference on machine learning, 6820-6829, 2020
2842020
Language independent authorship attribution with character level n-grams
F Peng, D Schuurmans, V Keselj, S Wang
10th Conference of the European Chapter of the Association for Computational …, 2003
279*2003
Combining Naive Bayes and n-Gram Language Models for Text Classification
F Peng, D Schuurmans
European Conference on Information Retrieval, 335-350, 2003
2762003
Reward augmented maximum likelihood for neural structured prediction
M Norouzi, S Bengio, N Jaitly, M Schuster, Y Wu, D Schuurmans
Advances In Neural Information Processing Systems 29, 2016
2522016
Understanding the impact of entropy on policy optimization
Z Ahmed, N Le Roux, M Norouzi, D Schuurmans
International conference on machine learning, 151-160, 2019
2362019
Algaedice: Policy gradient from arbitrary experience
O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans
arXiv preprint arXiv:1912.02074, 2019
2352019
Systolic peak detection in acceleration photoplethysmograms measured from emergency responders in tropical conditions
M Elgendi, I Norton, M Brearley, D Abbott, D Schuurmans
PloS one 8 (10), e76585, 2013
2272013
系统目前无法执行此操作,请稍后再试。
文章 1–20