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Found 5 papers, 4 papers with code
Automated Discovery of Local Rules for Desired Collective-Level Behavior Through Reinforcement Learning
We obtained this result by using recent advances in reinforcementlearning to systematically solve the inverse modeling problem: given an observedcollective behavior, we automatically find a policy generating it.
Supervised dimensionality reduction by a Linear Discriminant Analysis on pre-trained CNN features
The method finds the new classes close to the corresponding standard classes we took the data form.
Deep attention networks reveal the rules of collective motion in zebrafish
When using simulated trajectories, the model recovers the ground-truth interaction rule used to generate them, as well as the number of interacting neighbours.
Algebraic Machine Learning
Here we propose a different approach to learning and generalization that is parameter-free, fully discrete and that does not use function minimization.
idtracker.ai: Tracking all individuals in large collectives of unmarked animals
Our understanding of collective animal behavior is limited by our ability to track each of the individuals.
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