Παρακολούθηση
Alexander Kuhnle
Alexander Kuhnle
Zebra Technologies
Η διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα cantab.ac.uk - Αρχική σελίδα
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Παρατίθεται από
Παρατίθεται από
Έτος
A review on deep reinforcement learning for fluid mechanics
P Garnier, J Viquerat, J Rabault, A Larcher, A Kuhnle, E Hachem
Computers & Fluids 225, 104973, 2021
2732021
Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
H Tang, J Rabault, A Kuhnle, Y Wang, T Wang
Physics of Fluids 32 (5), 2020
2052020
Direct shape optimization through deep reinforcement learning
J Viquerat, J Rabault, A Kuhnle, H Ghraieb, A Larcher, E Hachem
Journal of Computational Physics 428, 110080, 2021
2042021
Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach
J Rabault, A Kuhnle
Physics of Fluids 31 (9), 2019
1862019
Tensorforce: a TensorFlow library for applied reinforcement learning
A Kuhnle, M Schaarschmidt, K Fricke
Web page, 2017
184*2017
ShapeWorld-A new test methodology for multimodal language understanding
A Kuhnle, A Copestake
arXiv preprint arXiv:1704.04517, 2017
762017
Comparative analysis of machine learning methods for active flow control
F Pino, L Schena, J Rabault, MA Mendez
Journal of Fluid Mechanics 958, A39, 2023
552023
LIFT: Reinforcement Learning in Computer Systems by Learning From Demonstrations
M Schaarschmidt, A Kuhnle, B Ellis, K Fricke, F Gessert, E Yoneki
arXiv preprint arXiv:1808.07903, 2018
472018
Resources for building applications with Dependency Minimal Recursion Semantics
A Copestake, G Emerson, MW Goodman, M Horvat, A Kuhnle, ...
Proceedings of the Tenth Language Resources and Evaluation Conference (LREC’16), 2016
332016
Policy-based optimization: single-step policy gradient method seen as an evolution strategy
J Viquerat, R Duvigneau, P Meliga, A Kuhnle, E Hachem
Neural Computing and Applications 35 (1), 449-467, 2023
232023
A Proposition-Based Abstractive Summariser
Y Fang, H Zhu, E Muszynska, A Kuhnle, S Teufel
23*
Tensorforce: a tensorflow library for applied reinforcement learning (2017)
A Kuhnle, M Schaarschmidt, K Fricke
Available online: tensorforce. readthedocs. io (accessed on 21 December 2021), 2019
142019
Deep learning evaluation using deep linguistic processing
A Kuhnle, A Copestake
arXiv preprint arXiv:1706.01322, 2017
122017
Reinforcement Learning for Information Retrieval
A Kuhnle, M Aroca-Ouellette, A Basu, M Sensoy, J Reid, D Zhang
Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021
112021
Going Beneath the Surface: Evaluating Image Captioning for Grammaticality, Truthfulness and Diversity
H Xie, T Sherborne, A Kuhnle, A Copestake
arXiv preprint arXiv:1912.08960, 2019
102019
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents
A Sestini, A Kuhnle, AD Bagdanov
2021 IEEE Conference on Games (CoG), 01-08, 2021
92021
Deep reinforcement learning applied to active flow control
J Rabault, A Kuhnle
ResearchGate Preprint https://doi. org/10.13140/RG 2 (10482.94404), 2020
82020
How clever is the FiLM model, and how clever can it be?
A Kuhnle, H Xie, A Copestake
European Conference on Computer Vision, 162-172, 2018
72018
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games
A Sestini, A Kuhnle, AD Bagdanov
arXiv preprint arXiv:2012.01914, 2020
62020
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments
A Sestini, A Kuhnle, AD Bagdanov
arXiv preprint arXiv:2012.02527, 2020
5*2020
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