Felix Leibfried
Felix Leibfried
Eisler Capital
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Uncertainty in neural networks: Approximately Bayesian ensembling
T Pearce, F Leibfried, A Brintrup, M Zaki, A Neely
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Bounded rationality, abstraction, and hierarchical decision-making: An information-theoretic optimality principle
T Genewein, F Leibfried, J Grau-Moya, DA Braun
Frontiers in Robotics and AI, 2015
Soft Q-learning with mutual-information regularization
J Grau-Moya, F Leibfried, P Vrancx
International Conference on Learning Representations (ICLR), 2019
A deep learning approach for joint video frame and reward prediction in Atari games
F Leibfried, N Kushman, K Hofmann
ICML Workshop on Principled Approaches to Deep Learning, 2017
Balancing two-player stochastic games with soft Q-learning
J Grau-Moya, F Leibfried, H Bou-Ammar
International Joint Conference on Artificial Intelligence (IJCAI), 2018
A tutorial on sparse Gaussian processes and variational inference
F Leibfried, V Dutordoir, ST John, N Durrande
arXiv preprint arXiv:2012.13962, 2020
Signaling equilibria in sensorimotor interactions
F Leibfried, J Grau-Moya, DA Braun
Cognition, 2015
A unified Bellman optimality principle combining reward maximization and empowerment
F Leibfried, S Pascual-Diaz, J Grau-Moya
Conference on Neural Information Processing Systems (NeurIPS), 2019
Planning with information-processing constraints and model uncertainty in Markov decision processes
J Grau-Moya, F Leibfried, T Genewein, DA Braun
European Conference on Machine Learning (ECML PKDD), 2016
An information-theoretic optimality principle for deep reinforcement learning
F Leibfried, J Grau-Moya, H Bou-Ammar
NeurIPS Workshop on Deep Reinforcement Learning, 2018
System architecture for an artificial intelligence platform
A Tukiainen, D Kim, T Nicholson, M Tomczak, JEMDC Flores, N Ferguson, ...
US Patent App. 16/753,580, 2020
GPflux: A library for deep Gaussian processes
V Dutordoir, H Salimbeni, E Hambro, J McLeod, F Leibfried, A Artemev, ...
arXiv preprint arXiv:2104.05674, 2021
Mutual-information regularization in Markov decision processes and actor-critic learning
F Leibfried, J Grau-Moya
Conference on Robot Learning (CoRL), 2019
Model-based regularization for deep reinforcement learning with transcoder networks
F Leibfried, P Vrancx
NeurIPS Workshop on Deep Reinforcement Learning, 2018
A reward-maximizing spiking neuron as a bounded rational decision maker
F Leibfried, DA Braun
Neural Computation, 2015
An information-theoretic on-line update principle for perception-action coupling
Z Peng, T Genewein, F Leibfried, DA Braun
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
Bounded rational decision-making in feedforward neural networks
F Leibfried, DA Braun
Conference on Uncertainty in Artificial Intelligence (UAI), 2016
Bellman: A toolbox for model-based reinforcement learning in TensorFlow
J McLeod, H Stojic, V Adam, D Kim, J Grau-Moya, P Vrancx, F Leibfried
arXiv preprint arXiv:2103.14407, 2021
Tuneable artificial intelligence
J Grau-Moya, F Leibfried, H Bou-Ammar
US Patent App. 16/759,241, 2020
Variational inference for model-free and model-based reinforcement learning
F Leibfried
arXiv preprint arXiv:2209.01693, 2022
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Artículos 1–20