Alexander Kuhnle
Alexander Kuhnle
Blue Prism
Verified email at - Homepage
Cited by
Cited by
Tensorforce: a TensorFlow library for applied reinforcement learning
A Kuhnle, M Schaarschmidt, K Fricke
Web page, 2017
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), 053605, 2020
Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach
J Rabault, A Kuhnle
Physics of Fluids 31 (9), 094105, 2019
ShapeWorld-A new test methodology for multimodal language understanding
A Kuhnle, A Copestake
arXiv preprint arXiv:1704.04517, 2017
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
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
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
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
A Proposition-Based Abstractive Summariser
Y Fang, H Zhu, E Muszynska, A Kuhnle, S Teufel
Deep learning evaluation using deep linguistic processing
A Kuhnle, A Copestake
arXiv preprint arXiv:1706.01322, 2017
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
Tensorforce: a tensorflow library for applied reinforcement learning (2017)
A Kuhnle, M Schaarschmidt, K Fricke
URL https://github. com/tensorforce/tensorforce, 2019
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
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games
A Sestini, A Kuhnle, AD Bagdanov
arXiv preprint arXiv:2012.01914, 2020
Modeling uncertain data using monads and an application to the sequence alignment problem
A Kuhnle
Bachelor’s thesis, Karlsruhe Institute of Technology, 2013
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
Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games
A Sestini, A Kuhnle, AD Bagdanov
arXiv preprint arXiv:2012.03532, 2020
Deep reinforcement learning applied to active flow control
J Rabault, A Kuhnle
ResearchGate Preprint https://doi. org/10.13140/RG 2 (10482.94404), 2020
Process Discovery for Structured Program Synthesis
D Zhang, A Kuhnle, J Richardson, M Sensoy
arXiv preprint arXiv:2008.05804, 2020
The meaning of" most" for visual question answering models
A Kuhnle, A Copestake
arXiv preprint arXiv:1812.11737, 2018
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