A Unified Approach to Interpreting Model Predictions

22 May 2017

Papers citing "A Unified Approach to Interpreting Model Predictions"

22 / 1,822 papers shown

Title
Minerva: A File-Based Ransomware Detector Dorjan Hitaj Giulio Pagnotta Fabio De Gaspari Lorenzo De Carli L. Mancini AAML 35 9 0 26 Jan 2023
Explaining Quantum Circuits with Shapley Values: Towards Explainable Quantum Machine Learning R. Heese Thore Gerlach Sascha Mucke Sabine Muller Matthias Jakobs Nico Piatkowski 33 18 0 22 Jan 2023
Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring Hué Sullivan Hurlin Christophe Pérignon Christophe Saurin Sébastien 42 0 0 12 Dec 2022
Psychophysiology-aided Perceptually Fluent Speech Analysis of Children Who Stutter Yi Xiao Harshit Sharma V. Tumanova Asif Salekin 82 0 0 16 Nov 2022
Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT Jiaheng Xie Xiaohang Zhao Xiang Liu Xiao Fang OOD 90 2 0 08 Nov 2022
VISTANet: VIsual Spoken Textual Additive Net for Interpretable Multimodal Emotion Recognition Puneet Kumar Sarthak Malik Balasubramanian Raman Xiaobai Li 81 2 0 24 Aug 2022
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence Frederik Pahde Maximilian Dreyer Leander Weber Moritz Weckbecker Christopher J. Anders Thomas Wiegand Wojciech Samek Sebastian Lapuschkin 87 9 0 07 Feb 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 221 190 0 03 Feb 2022
Uncovering the Dark Side of Telegram: Fakes, Clones, Scams, and Conspiracy Movements Massimo La Morgia Alessandro Mei Alberto Maria Mongardini Jie Wu 28 21 0 26 Nov 2021
Rule Generation for Classification: Scalability, Interpretability, and Fairness Tabea E. Rober Adia C. Lumadjeng M. Akyuz cS. .Ilker Birbil 78 2 0 21 Apr 2021
On the Tractability of SHAP Explanations Guy Van den Broeck A. Lykov Maximilian Schleich Dan Suciu FAtt TDI 45 266 0 18 Sep 2020
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations Sebastian Mežnar Nada Lavrac Blaž Škrlj 54 5 0 08 Sep 2020
Explainability in Deep Reinforcement Learning Alexandre Heuillet Fabien Couthouis Natalia Díaz Rodríguez XAI 91 279 0 15 Aug 2020
Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay Sasha Rubin Thomas Gerspacher Martin C. Cooper Alexey Ignatiev Nina Narodytska FAtt 39 61 0 13 Aug 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks Kirill Bykov Marina M.-C. Höhne Klaus-Robert Muller Shinichi Nakajima Marius Kloft UQCV FAtt 53 31 0 16 Jun 2020
Explainable AI for a No-Teardown Vehicle Component Cost Estimation: A Top-Down Approach A. Moawad E. Islam Namdoo Kim R. Vijayagopal A. Rousseau Wei Biao Wu 44 5 0 15 Jun 2020
SurvLIME: A method for explaining machine learning survival models M. Kovalev Lev V. Utkin E. Kasimov 161 90 0 18 Mar 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 78 82 0 17 Mar 2020
A Supervised Machine Learning Model For Imputing Missing Boarding Stops In Smart Card Data Nadav Shalit Michael Fire Eran Ben-Elia 110 4 0 10 Mar 2020
Two Decades of AI4NETS-AI/ML for Data Networks: Challenges & Research Directions P. Casas GNN 28 8 0 03 Mar 2020
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 82 3,848 0 10 Apr 2017
Not Just a Black Box: Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Shcherbina A. Kundaje FAtt 40 782 0 05 May 2016