- Sprache
-
Englisch
- Identifier
-
1272689018
Thema
Künstliche Intelligenz; Maschinelles Lernen; Bestärkendes Lernen; Markov-Entscheidungsprozess
Beteiligte Personen und Organisationen
Biedenkapp, André
Hutter, Frank
Lindauer, Marius
Hutter, Frank
Doerr, Carola
Albert-Ludwigs-Universität Freiburg / Institut für Informatik
Maschinelles Lernen und Natürlichsprachliche Systeme, Professur Frank Hutter
Albert-Ludwigs-Universität Freiburg / Fakultät für Angewandte Wissenschaften
Hutter, Frank
Lindauer, Marius
Hutter, Frank
Doerr, Carola
Albert-Ludwigs-Universität Freiburg / Institut für Informatik
Maschinelles Lernen und Natürlichsprachliche Systeme, Professur Frank Hutter
Albert-Ludwigs-Universität Freiburg / Fakultät für Angewandte Wissenschaften
- DOI
-
10.6094/UNIFR/230869
- URN
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
28.02.2023, 11:39 MEZ
Objekttyp
- Monografie
Beteiligte
- Biedenkapp, André
- Hutter, Frank
- Lindauer, Marius
- Hutter, Frank
- Doerr, Carola
- Albert-Ludwigs-Universität Freiburg / Institut für Informatik
- Maschinelles Lernen und Natürlichsprachliche Systeme, Professur Frank Hutter
- Albert-Ludwigs-Universität Freiburg / Fakultät für Angewandte Wissenschaften
Ähnliche Objekte (12)
Deep learning for brain-signal decoding from electroencephalography
Stable deep reinforcement learning
On the role of time horizons in reinforcement learning
Efficient bayesian hyperparameter optimization
Simultaneous estimation of rewards and dynamics in inverse reinforcement learning problems
Efficient and practical neural architecture search
Learning navigation policies with deep reinforcement learning
A multi-template atlas for volumetric images
Towards simulating carcinogenesis : : the hallmark concept revisited
Machine learning methods for brain state-dependent motor rehabilitation after stroke
Leveraging sparse and dense features for reliable state estimation in urban environments
Learning mobile manipulation actions from human demonstrations: an approach to learning and augmenting action models and their integration into task representations
Deep learning for brain-signal decoding from electroencephalography
Stable deep reinforcement learning
On the role of time horizons in reinforcement learning
Efficient bayesian hyperparameter optimization
Simultaneous estimation of rewards and dynamics in inverse reinforcement learning problems
Efficient and practical neural architecture search
Learning navigation policies with deep reinforcement learning
A multi-template atlas for volumetric images
Towards simulating carcinogenesis : : the hallmark concept revisited
Machine learning methods for brain state-dependent motor rehabilitation after stroke
Leveraging sparse and dense features for reliable state estimation in urban environments
Learning mobile manipulation actions from human demonstrations: an approach to learning and augmenting action models and their integration into task representations
Deep learning for brain-signal decoding from electroencephalography
Stable deep reinforcement learning
On the role of time horizons in reinforcement learning
Efficient bayesian hyperparameter optimization
Simultaneous estimation of rewards and dynamics in inverse reinforcement learning problems
Efficient and practical neural architecture search
Learning navigation policies with deep reinforcement learning
A multi-template atlas for volumetric images
Towards simulating carcinogenesis : : the hallmark concept revisited
Machine learning methods for brain state-dependent motor rehabilitation after stroke
Leveraging sparse and dense features for reliable state estimation in urban environments