3 citations · 5 across the 7 of their papers we have counts for
6 papers · 1 filter
Motion Equivariant Convolutional LSTM: When Motion Becomes a Gauge Symmetry
Vahid Hosseinzadeh, T. Anderson Keller, Michael Puthawala
Objects move, cameras pan, and weather drifts with the wind: much of what changes in sequential data is motion. Motion changes where things are, not what they are, and is therefore…
Where the Score Lives: A Wavelet View of Diffusion
Emma Finn, Binxu Wang, T. Anderson Keller +1
Score-based generative models have had remarkable success over the last decade in generating a diverse set of visually plausible images. A variety of architectures including CNNs,…
Kuramoto Orientation Diffusion Models
Yue Song, T. Anderson Keller, Sevan Brodjian +4
Orientation-rich images, such as fingerprints and textures, often exhibit coherent angular directional patterns that are challenging to model using standard generative approaches b…
Flow Equivariant Recurrent Neural Networks
T. Anderson Keller
Data arrives at our senses as a continuous stream, smoothly transforming from one instant to the next. These smooth transformations can be viewed as continuous symmetries of the en…
Langevin Flows for Modeling Neural Latent Dynamics
Yue Song, T. Anderson Keller, Yisong Yue +2
Neural populations exhibit latent dynamical structures that drive time-evolving spiking activities, motivating the search for models that capture both intrinsic network dynamics an…
Unsupervised Representation Learning from Sparse Transformation Analysis
Yue Song, Thomas Anderson Keller, Yisong Yue +2
There is a vast literature on representation learning based on principles such as coding efficiency, statistical independence, causality, controllability, or symmetry. In this pape…