Alexander Kuhnle
TitleCited byYear
Tensorforce: a TensorFlow library for applied reinforcement learning
A Kuhnle, M Schaarschmidt, K Fricke
Web page, 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
ShapeWorld-A new test methodology for multimodal language understanding
A Kuhnle, A Copestake
arXiv preprint arXiv:1704.04517, 2017
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 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
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
Modeling uncertain data using monads and an application to the sequence alignment problem
A Kuhnle
Master’s thesis, Karlsruhe Institute of Technology, 2013
Accelerating Deep Reinforcement Learning of Active Flow Control strategies through a multi-environment approach
J Rabault, A Kuhnle
arXiv preprint arXiv:1906.10382, 2019
Accelerating deep reinforcement learning strategies of flow control through a multi-environment approach
J Rabault, A Kuhnle
Physics of Fluids 31 (9), 094105, 2019
Direct shape optimization through deep reinforcement learning
J Viquerat, J Rabault, A Kuhnle, H Ghraieb, E Hachem
arXiv preprint arXiv:1908.09885, 2019
What is needed for simple spatial language capabilities in VQA?
A Kuhnle, A Copestake
arXiv preprint arXiv:1908.06336, 2019
A review on Deep Reinforcement Learning for Fluid Mechanics
P Garnier, J Viquerat, J Rabault, A Larcher, A Kuhnle, E Hachem
arXiv preprint arXiv:1908.04127, 2019
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games
A Sestini, A Kuhnle, AD Bagdanov
The meaning of" most" for visual question answering models
A Kuhnle, A Copestake
arXiv preprint arXiv:1812.11737, 2018
The meaning of
A Kuhnle, A Copestake
Research proposal: Evaluating multi-modal deep learning systems with micro-worlds
A Kuhnle
Active learning to rank for assessing the linguistic quality of sentences
A Kuhnle
Investigating the effect of controlled context choice in distributional semantics
A Kuhnle
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