Universality and individuality in neural dynamics across large populations of recurrent networks

no code implementations • NeurIPS 2019 • Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

To address these foundational questions, we study populations of thousands of networks, with commonly used RNN architectures, trained to solve neuroscientifically motivated tasks and characterize their nonlinear dynamics.

Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics

no code implementations • NeurIPS 2019 • Niru Maheswaranathan, Alex Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

In this work, we use tools from dynamical systems analysis to reverse engineer recurrent networks trained to perform sentiment classification, a foundational natural language processing task.

General Classification Sentiment Analysis +1

Line attractor dynamics in recurrent networks for sentiment classification

no code implementations • ICML Workshop Deep_Phenomen 2019 • Niru Maheswaranathan, Alex H. Williams, Matthew D. Golub, Surya Ganguli, David Sussillo

Recurrent neural networks (RNNs) are a powerful tool for modeling sequential data.