3 papers
stat.ML2026
Memory by Design: Probabilistic Sequence Layers
Matthew Dowling, Hyungju Jeon, Cristina Savin +1
We introduce the \emph{design-model framework}: a way to derive efficient recurrent sequence maps from explicit assumptions about memory. A design model writes evidence into memory…
stat.ML2025
Disentangled representations via score-based variational autoencoders
Benjamin S. H. Lyo, Eero P. Simoncelli, Cristina Savin
We present the Score-based Autoencoder for Multiscale Inference (SAMI), a method for unsupervised representation learning that combines the theoretical frameworks of diffusion mode…
cs.CV2025
Learning predictable and robust neural representations by straightening image sequences
Xueyan Niu, Cristina Savin, Eero P. Simoncelli
Prediction is a fundamental capability of all living organisms, and has been proposed as an objective for learning sensory representations. Recent work demonstrates that in primate…