Suivre
Janos Kramar
Janos Kramar
DeepMind
Adresse e-mail validée de google.com
Titre
Citée par
Citée par
Année
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
3772016
Reinforcement and imitation learning for diverse visuomotor skills
Y Zhu, Z Wang, J Merel, A Rusu, T Erez, S Cabi, S Tunyasuvunakool, ...
arXiv preprint arXiv:1802.09564, 2018
3402018
OpenSpiel: A framework for reinforcement learning in games
M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ...
arXiv preprint arXiv:1908.09453, 2019
2252019
Guidelines for artificial intelligence containment
J Babcock, J Kramar, RV Yampolskiy
Next-Generation Ethics: Engineering a Better Society (Ed.) Ali. E. Abbas, 90-112, 2019
602019
The AGI containment problem
J Babcock, J Kramár, R Yampolskiy
Artificial General Intelligence: 9th International Conference, AGI 2016, New …, 2016
572016
Learning reciprocity in complex sequential social dilemmas
T Eccles, E Hughes, J Kramár, S Wheelwright, JZ Leibo
arXiv preprint arXiv:1903.08082, 2019
472019
Learning to play no-press diplomacy with best response policy iteration
T Anthony, T Eccles, A Tacchetti, J Kramár, I Gemp, T Hudson, N Porcel, ...
Advances in Neural Information Processing Systems 33, 17987-18003, 2020
442020
Negotiation and honesty in artificial intelligence methods for the board game of Diplomacy
J Kramár, T Eccles, I Gemp, A Tacchetti, KR McKee, M Malinowski, ...
Nature Communications 13 (1), 7214, 2022
312022
Tracr: Compiled transformers as a laboratory for interpretability
D Lindner, J Kramár, S Farquhar, M Rahtz, T McGrath, V Mikulik
Advances in Neural Information Processing Systems 36, 2024
272024
Does circuit analysis interpretability scale? evidence from multiple choice capabilities in chinchilla
T Lieberum, M Rahtz, J Kramár, G Irving, R Shah, V Mikulik
arXiv preprint arXiv:2307.09458, 2023
232023
OpenSpiel: a framework for reinforcement learning in games. CoRR abs/1908.09453 (2019)
M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ...
arXiv preprint arXiv:1908.09453, 2019
232019
Reinforcement and imitation learning for a task
S Tunyasuvunakool, Y Zhu, J Merel, J Kramar, Z Wang, NMO Heess
US Patent App. 16/174,112, 2019
222019
Sample-based approximation of Nash in large many-player games via gradient descent
I Gemp, R Savani, M Lanctot, Y Bachrach, T Anthony, R Everett, ...
arXiv preprint arXiv:2106.01285, 2021
152021
Explaining grokking through circuit efficiency
V Varma, R Shah, Z Kenton, J Kramár, R Kumar
arXiv preprint arXiv:2309.02390, 2023
142023
The hydra effect: Emergent self-repair in language model computations
T McGrath, M Rahtz, J Kramar, V Mikulik, S Legg
arXiv preprint arXiv:2307.15771, 2023
142023
The Imitation Game: Learned Reciprocity in Markov games.
T Eccles, E Hughes, J Kramár, S Wheelwright, JZ Leibo
AAMAS 19, 3, 2019
112019
Power-seeking can be probable and predictive for trained agents
V Krakovna, J Kramar
arXiv preprint arXiv:2304.06528, 2023
72023
A generalized-zero-preserving method for compact encoding of concept lattices
M Skala, V Krakovna, J Kramár, G Penn
Proceedings of the 48th annual meeting of the Association for Computational …, 2010
62010
How intelligible is intelligence?
A Salamon, S Rayhawk, J Kramár
Proceedings of the VIII European conference on computing and philosophy …, 2010
42010
Designing all-pay auctions using deep learning and multi-agent simulation
I Gemp, T Anthony, J Kramar, T Eccles, A Tacchetti, Y Bachrach
Scientific Reports 12 (1), 16937, 2022
32022
Le système ne peut pas réaliser cette opération maintenant. Veuillez réessayer plus tard.
Articles 1–20