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Efficient non-conjugate Gaussian process factor models for spike count
  data using polynomial approximations

Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations

7 June 2019
Stephen L. Keeley
D. Zoltowski
Yiyi Yu
Jacob L. Yates
S. L. Smith
Jonathan W. Pillow
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Papers citing "Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations"

3 / 3 papers shown
Title
Nonnegative spatial factorization
Nonnegative spatial factorization
F. W. Townes
Barbara E. Engelhardt
18
11
0
12 Oct 2021
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
55
87
0
09 Sep 2021
Bayesian latent structure discovery from multi-neuron recordings
Bayesian latent structure discovery from multi-neuron recordings
Scott W. Linderman
Ryan P. Adams
Jonathan W. Pillow
13
54
0
26 Oct 2016
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