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Discovering how ice crystals grow using neural ODEs and symbolic regression. [PDF]

Sci Adv
Lamb KD, Harrington JY, Moyle AM, Pokrifka GF, Clouser BW, Ebert V, Möhler O, Saathoff H.   +7 more
europepmc   +1 more source

Introduction of R&D Environments for Synthetic Q-space Learning : An Instance

Japanese Journal of Magnetic Resonance in Medicine
openaire   +1 more source

Data generation and machine learning-based image restoration in 3D synthetic environments

openaire   +1 more source

Learning Dense Correspondence from Synthetic Environments

2022 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2022
Estimation of human shape and pose from a single image is a challenging task. It is an even more difficult problem to map the identified human shape onto a 3D human model. Existing methods map manually labelled human pixels in real 2D images onto the 3D surface, which is prone to human error, and the sparsity of available annotated data often leads to ...
Lars Petersson, Nariman Habili, Olivier Salvado   +2 more
exaly   +4 more sources

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Synthetic learning agents in game-playing social environments

Adaptive Behavior, 2016
This paper investigates the performance of synthetic agents in playing and learning scenarios in a turn-based zero-sum game and highlights the ability of opponent-based learning models to demonstrate competitive playing performances in social environments. Synthetic agents are generated based on a variety of combinations of some key parameters, such as
Chairi Kiourt, Dimitris Kalles
exaly   +2 more sources