Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics

25 June 2019

Papers citing "Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics"

8 / 8 papers shown

Title
Trainability, Expressivity and Interpretability in Gated Neural ODEs T. Kim T. Can K. Krishnamurthy AI4CE 35 4 0 12 Jul 2023
From Data-Fitting to Discovery: Interpreting the Neural Dynamics of Motor Control through Reinforcement Learning Eugene R. Rush Kaushik Jayaram J. Humbert 8 1 0 18 May 2023
Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management Charl Maree C. Omlin 21 1 0 20 Apr 2022
Understanding How Encoder-Decoder Architectures Attend Kyle Aitken V. Ramasesh Yuan Cao Niru Maheswaranathan 23 17 0 28 Oct 2021
Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation Charl Maree C. Omlin AI4TS 8 5 0 24 Sep 2021
Representation learning for neural population activity with Neural Data Transformers Joel Ye C. Pandarinath AI4TS AI4CE 11 51 0 02 Aug 2021
Meta-trained agents implement Bayes-optimal agents Vladimir Mikulik Grégoire Delétang Tom McGrath Tim Genewein Miljan Martic Shane Legg Pedro A. Ortega OOD FedML 27 40 0 21 Oct 2020
How recurrent networks implement contextual processing in sentiment analysis Niru Maheswaranathan David Sussillo 14 22 0 17 Apr 2020