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ORCIDKristopher T. Jensen
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2020 – today
- 2023
[c5]Martin Bjerke, Lukas Schott, Kristopher T. Jensen, Claudia Battistin, David A. Klindt, Benjamin Adric Dunn:
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles. ICLR 2023- [i4]Kristopher T. Jensen:
An introduction to reinforcement learning for neuroscience. CoRR abs/2311.07315 (2023) - 2022
- [c4]Marine Schimel, Ta-Chu Kao, Kristopher T. Jensen, Guillaume Hennequin:
iLQR-VAE : control-based learning of input-driven dynamics with applications to neural data. ICLR 2022 - [i3]Martin Bjerke, Lukas Schott, Kristopher T. Jensen, Claudia Battistin
, David A. Klindt, Benjamin A. Dunn:
Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles. CoRR abs/2210.03155 (2022) - 2021
- [c3]Kristopher T. Jensen, Ta-Chu Kao, Jasmine Stone, Guillaume Hennequin:
Scalable Bayesian GPFA with automatic relevance determination and discrete noise models. NeurIPS 2021: 10613-10626 - [c2]Ta-Chu Kao, Kristopher T. Jensen, Gido van de Ven, Alberto Bernacchia, Guillaume Hennequin:
Natural continual learning: success is a journey, not (just) a destination. NeurIPS 2021: 28067-28079 - [i2]Ta-Chu Kao, Kristopher T. Jensen, Alberto Bernacchia, Guillaume Hennequin:
Natural continual learning: success is a journey, not (just) a destination. CoRR abs/2106.08085 (2021) - 2020
- [c1]Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin:
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. NeurIPS 2020 - [i1]Kristopher T. Jensen, Ta-Chu Kao, Marco Tripodi, Guillaume Hennequin:
Manifold GPLVMs for discovering non-Euclidean latent structure in neural data. CoRR abs/2006.07429 (2020)
Coauthor Index
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