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https://cbl.eng.cam.ac.uk/publications/hennequin-nips-2014.pdf25 Jun 2024: laurence@gatsby.ucl.ac.uk. Máté Lengyel1. m.lengyel@eng.cam.ac.uk. 1Computational & Biological Learning Lab, Dept. of Engineering, University of Cambridge, UK2Gatsby Computational Neuroscience Unit, University College London, UK. ... Máté Lengyel1 -
Neuroscience out of control:control-theoretic perspectives on neural…
https://cbl.eng.cam.ac.uk/publications/kao-cur-op-neurobiol-2019.pdf25 Jun 2024: More generally, dissecting the circuit basis of com-plex computations such as motor control or reinforcementlearning will benefit from a deeper integration of control the-ory, machine learning, and neuroscience. ... Cunningham. “Towards the neu-ral -
ILQR-VAE : CONTROL-BASED LEARNING OF INPUT-DRIVEN DYNAMICS WITH…
https://cbl.eng.cam.ac.uk/publications/schimel-iclr-2022.pdf25 Jun 2024: While iLQR-VAE could find applications in many fields as a general approach to learning stochasticnonlinear dynamical systems, here we focus on neuroscience case studies. ... DeepLabCut: markerless pose estimation of user-defined body parts with deep -
Manifold GPLVMs for discovering non-Euclideanlatent structure in…
https://cbl.eng.cam.ac.uk/publications/jensen-neurips-2020.pdf25 Jun 2024: Neural computation, 25:626–649. Titsias, M. K. (2009). Variational learning of inducing variables in sparse Gaussian processes. ... To highlight the importance of unsupervised non-Euclidean learning methods in neuroscience and toillustrate the
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