1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2023★ 1 cited
Probabilistic Unrolling: Scalable, Inverse-Free Maximum Likelihood Estimation for Latent Gaussian Models
Alexander Lin, Bahareh Tolooshams, Yves Atchadé +1
Latent Gaussian models have a rich history in statistics and machine learning, with applications ranging from factor analysis to compressed sensing to time series analysis. The cla…
cs.LG2023
Learning Linear Groups in Neural Networks
Emmanouil Theodosis, Karim Helwani, Demba Ba
Employing equivariance in neural networks leads to greater parameter efficiency and improved generalization performance through the encoding of domain knowledge in the architecture…
cs.AI2023
Sparse, Geometric Autoencoder Models of V1
Jonathan Huml, Abiy Tasissa, Demba Ba
The classical sparse coding model represents visual stimuli as a linear combination of a handful of learned basis functions that are Gabor-like when trained on natural image data.…