Interpreting recurrent neural networks behaviour via excitable network
attractors

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Interpreting recurrent neural networks behaviour via excitable network attractors

27 July 2018

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Papers citing "Interpreting recurrent neural networks behaviour via excitable network attractors"

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Title
Recent Advances in Malware Detection: Graph Learning and Explainability Hossein Shokouhinejad Roozbeh Razavi-Far Hesamodin Mohammadian Mahdi Rabbani Samuel Ansong Griffin Higgins Ali Ghorbani AAML 132 2 0 14 Feb 2025
Seeing double with a multifunctional reservoir computer Andrew Flynn V. Tsachouridis Andreas Amann 53 8 0 09 May 2023
Outdoor Monocular Depth Estimation: A Research Review Pulkit Vyas Chirag Saxena Anwesh Badapanda Anurag Goswami MDE 97 15 0 03 May 2022
Reinforcement Learning with Intrinsic Affinity for Personalized Prosperity Management Charl Maree C. Omlin 83 1 0 20 Apr 2022
Scope and Sense of Explainability for AI-Systems Anastasia-Maria Leventi-Peetz T. Östreich Werner Lennartz Kai Weber 79 5 0 20 Dec 2021
Understanding Spending Behavior: Recurrent Neural Network Explanation and Interpretation Charl Maree C. Omlin AI4TS 50 5 0 24 Sep 2021
Warming up recurrent neural networks to maximise reachable multistability greatly improves learning Gaspard Lambrechts Florent De Geeter Nicolas Vecoven D. Ernst G. Drion 47 3 0 02 Jun 2021
Continual Learning for Recurrent Neural Networks: an Empirical Evaluation Andrea Cossu Antonio Carta Vincenzo Lomonaco D. Bacciu CLL 93 110 0 12 Mar 2021
Input-to-State Representation in linear reservoirs dynamics Pietro Verzelli Cesare Alippi L. Livi Peter Tiño 61 9 0 24 Mar 2020
Evaluating complexity and resilience trade-offs in emerging memory inference machines C. Bennett Ryan Dellana T. Xiao Ben Feinberg S. Agarwal S. Cardwell M. Marinella William M. Severa Brad Aimone 37 2 0 25 Feb 2020
Echo State Networks with Self-Normalizing Activations on the Hyper-Sphere Pietro Verzelli Cesare Alippi L. Livi 100 23 0 27 Mar 2019