231 citations · 759 across the 17 of their papers we have counts for
7 papers · 1 filter
Analyzing Populations of Neural Networks via Dynamical Model Embedding
Jordan Cotler, Kai Sheng Tai, Felipe Hernández +2
A core challenge in the interpretation of deep neural networks is identifying commonalities between the underlying algorithms implemented by distinct networks trained for the same…
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
Jimmy T. H. Smith, Scott W. Linderman, David Sussillo
Recurrent neural networks (RNNs) are powerful models for processing time-series data, but it remains challenging to understand how they function. Improving this understanding is of…
Reverse engineering learned optimizers reveals known and novel mechanisms
Niru Maheswaranathan, David Sussillo, Luke Metz +2
Learned optimizers are algorithms that can themselves be trained to solve optimization problems. In contrast to baseline optimizers (such as momentum or Adam) that use simple updat…
The geometry of integration in text classification RNNs
Kyle Aitken, Vinay V. Ramasesh, Ankush Garg +3
Despite the widespread application of recurrent neural networks (RNNs) across a variety of tasks, a unified understanding of how RNNs solve these tasks remains elusive. In particul…
Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics
Niru Maheswaranathan, Alex Williams, Matthew D. Golub +2
Recurrent neural networks (RNNs) are a widely used tool for modeling sequential data, yet they are often treated as inscrutable black boxes. Given a trained recurrent network, we w…
LFADS - Latent Factor Analysis via Dynamical Systems
David Sussillo, Rafal Jozefowicz, L. F. Abbott +1
Neuroscience is experiencing a data revolution in which many hundreds or thousands of neurons are recorded simultaneously. Currently, there is little consensus on how such data sho…