2 citations · 3 across the 5 of their papers we have counts for
5 papers
Manifolds and Modules: How Function Develops in a Neural Foundation Model
Johannes Bertram, Luciano Dyballa, T. Anderson Keller +2
Foundation models have shown remarkable success in fitting biological visual systems; however, their black-box nature inherently limits their utility for understanding brain functi…
Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders
T. Anderson Keller, Qinghe Gao, Max Welling
Category-selectivity in the brain describes the observation that certain spatially localized areas of the cerebral cortex tend to respond robustly and selectively to stimuli from s…
As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning
Fiorella Wever, T. Anderson Keller, Laura Symul +1
High levels of missing data and strong class imbalance are ubiquitous challenges that are often presented simultaneously in real-world time series data. Existing methods approach t…
Self Normalizing Flows
T. Anderson Keller, Jorn W. T. Peters, Priyank Jaini +3
Efficient gradient computation of the Jacobian determinant term is a core problem in many machine learning settings, and especially so in the normalizing flow framework. Most propo…
Fast Weight Long Short-Term Memory
T. Anderson Keller, Sharath Nittur Sridhar, Xin Wang
Associative memory using fast weights is a short-term memory mechanism that substantially improves the memory capacity and time scale of recurrent neural networks (RNNs). As recent…