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Manifold GPLVMs for discovering non-Euclidean latent structure in neural
  data

Manifold GPLVMs for discovering non-Euclidean latent structure in neural data

12 June 2020
Kristopher T. Jensen
Ta-Chu Kao
Marco Tripodi
Guillaume Hennequin
    DRL
ArXivPDFHTML

Papers citing "Manifold GPLVMs for discovering non-Euclidean latent structure in neural data"

4 / 4 papers shown
Title
Stationary Kernels and Gaussian Processes on Lie Groups and their
  Homogeneous Spaces I: the compact case
Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces I: the compact case
I. Azangulov
A. Smolensky
Alexander Terenin
Viacheslav Borovitskiy
39
21
0
31 Aug 2022
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions
GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions
Ryan Lopez
P. Atzberger
AI4CE
24
7
0
10 Jun 2022
Neural Latents Benchmark '21: Evaluating latent variable models of
  neural population activity
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
Felix Pei
Joel Ye
D. Zoltowski
Anqi Wu
Raeed H. Chowdhury
...
L. Miller
Jonathan W. Pillow
Il Memming Park
Eva L. Dyer
C. Pandarinath
47
86
0
09 Sep 2021
Natural continual learning: success is a journey, not (just) a
  destination
Natural continual learning: success is a journey, not (just) a destination
Ta-Chu Kao
Kristopher T. Jensen
Gido M. van de Ven
A. Bernacchia
Guillaume Hennequin
CLL
14
46
0
15 Jun 2021
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