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Martin Schmid
Martin Schmid
Google DeepMind
Dirección de correo verificada de kam.mff.cuni.cz - Página principal
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Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
M Moravčík, M Schmid, N Burch, V Lisý, D Morrill, N Bard, T Davis, ...
Science 356 (6337), 508-513, 2017
8632017
Text understanding with the attention sum reader network
R Kadlec, M Schmid, O Bajgar, J Kleindienst
arXiv preprint arXiv:1603.01547, 2016
3172016
Improved deep learning baselines for ubuntu corpus dialogs
R Kadlec, M Schmid, J Kleindienst
arXiv preprint arXiv:1510.03753, 2015
1212015
Variance reduction in monte carlo counterfactual regret minimization (VR-MCCFR) for extensive form games using baselines
M Schmid, N Burch, M Lanctot, M Moravcik, R Kadlec, M Bowling
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 2157-2164, 2019
522019
Rethinking formal models of partially observable multiagent decision making
V Kovařík, M Schmid, N Burch, M Bowling, V Lisý
Artificial Intelligence 303, 103645, 2022
412022
Refining subgames in large imperfect information games
M Moravcik, M Schmid, K Ha, M Hladik, S Gaukrodger
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
372016
Revisiting CFR+ and alternating updates
N Burch, M Moravcik, M Schmid
Journal of Artificial Intelligence Research 64, 429-443, 2019
342019
Deepstack: expert-level artificial intelligence in no-limit poker. CoRR abs/1701.01724 (2017)
M Moravcík, M Schmid, N Burch, V Lisý, D Morrill, N Bard, T Davis, ...
arXiv preprint arXiv:1701.01724, 2017
262017
Automatic question generation from natural text
A Kantor, J Kleindienst, M Schmid
US Patent 9,904,675, 2018
232018
Player of games
M Schmid, M Moravcik, N Burch, R Kadlec, J Davidson, K Waugh, N Bard, ...
arXiv preprint arXiv:2112.03178, 2021
192021
Aivat: A new variance reduction technique for agent evaluation in imperfect information games
N Burch, M Schmid, M Moravcik, D Morill, M Bowling
Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018
152018
Low-variance and zero-variance baselines for extensive-form games
T Davis, M Schmid, M Bowling
International Conference on Machine Learning, 2392-2401, 2020
142020
Bounding the support size in extensive form games with imperfect information
M Schmid, M Moravcik, M Hladik
Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014
132014
The advantage regret-matching actor-critic
A Gruslys, M Lanctot, R Munos, F Timbers, M Schmid, J Perolat, D Morrill, ...
arXiv preprint arXiv:2008.12234, 2020
122020
Approximate exploitability: Learning a best response in large games
F Timbers, E Lockhart, M Lanctot, M Schmid, J Schrittwieser, T Hubert, ...
arXiv preprint arXiv:2004.09677, 2020
122020
Solving common-payoff games with approximate policy iteration
S Sokota, E Lockhart, F Timbers, E Davoodi, R D'Orazio, N Burch, ...
Proceedings of the AAAI Conference on Artificial Intelligence 35 (11), 9695-9703, 2021
102021
Sound search in imperfect information games
M Sustr, M Schmid, M Moravcik, N Burch, M Lanctot, M Bowling
7*2020
Search in Imperfect Information Games
M Schmid
arXiv preprint arXiv:2111.05884, 2021
32021
Multiple-point cognitive identity challenge system
DS Anderson, OCW Blodgett, T Durniak, MR Moore, M Schmid
US Patent 10,210,317, 2019
32019
Neural Text Understanding with Attention Sum Reader
R Kadlec, M Schmid, O Bajgar, J Kleindienst
32016
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Artículos 1–20