Evaluation of Interpretability for Deep Learning algorithms in EEG
Emotion Recognition: A case study in Autism

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Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism

25 November 2021

J. M. M. Torres

Sara E. Medina-DeVilliers

Giuseppe Riccardi

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Papers citing "Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism"

16 / 16 papers shown

Title
Interpretable SincNet-based Deep Learning for Emotion Recognition from EEG brain activity J. M. M. Torres Mirco Ravanelli Sara E. Medina-DeVilliers M. Lerner Giuseppe Riccardi 39 21 0 18 Jul 2021
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation D. Powers 173 5,288 0 11 Oct 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 112 83 0 17 Mar 2020
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps Beomsu Kim Junghoon Seo Seunghyun Jeon Jamyoung Koo J. Choe Taegyun Jeon FAtt 54 70 0 13 Feb 2019
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 141 1,970 0 08 Oct 2018
iNNvestigate neural networks! Maximilian Alber Sebastian Lapuschkin P. Seegerer Miriam Hagele Kristof T. Schütt G. Montavon Wojciech Samek K. Müller Sven Dähne Pieter-Jan Kindermans 59 348 0 13 Aug 2018
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 101 687 0 02 Nov 2017
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks Aditya Chattopadhyay Anirban Sarkar Prantik Howlader V. Balasubramanian FAtt 112 2,306 0 30 Oct 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 291 2,266 0 24 Jun 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 204 2,235 0 12 Jun 2017
Learning how to explain neural networks: PatternNet and PatternAttribution Pieter-Jan Kindermans Kristof T. Schütt Maximilian Alber K. Müller D. Erhan Been Kim Sven Dähne XAI FAtt 76 340 0 16 May 2017
Mask R-CNN Kaiming He Georgia Gkioxari Piotr Dollár Ross B. Girshick ObjD 352 27,230 0 20 Mar 2017
Deep learning with convolutional neural networks for EEG decoding and visualization Robin Tibor Schirrmeister Jost Tobias Springenberg Lukas Dominique Josef Fiederer Martin Glasstetter Katharina Eggensperger Michael Tangermann Frank Hutter Wolfram Burgard Tonio Ball 72 1,924 0 15 Mar 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 321 20,070 0 07 Oct 2016
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.9K 150,260 0 22 Dec 2014
Sequence to Sequence Learning with Neural Networks Ilya Sutskever Oriol Vinyals Quoc V. Le AIMat 437 20,584 0 10 Sep 2014

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