Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification

3 April 2025

Papers citing "Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification"

20 / 20 papers shown

Title
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions Luca Longo Mario Brcic Federico Cabitza Jaesik Choi Roberto Confalonieri ... Andrés Páez Wojciech Samek Johannes Schneider Timo Speith Simone Stumpf 81 211 0 30 Oct 2023
Input-gradient space particle inference for neural network ensembles Trung Trinh Markus Heinonen Luigi Acerbi Samuel Kaski UQCV 60 4 0 05 Jun 2023
Mitigating Molecular Aggregation in Drug Discovery with Predictive Insights from Explainable AI Hunter Sturm Jonas Teufel Kaitlin A. Isfeld Pascal Friederich Rebecca Davis 40 2 0 03 Jun 2023
Optimal Training of Mean Variance Estimation Neural Networks Laurens Sluijterman Eric Cator Tom Heskes DRL 52 26 0 17 Feb 2023
A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges Mario Alfonso Prado-Romero Bardh Prenkaj Giovanni Stilo F. Giannotti CML 91 32 0 21 Oct 2022
Learning and Evaluating Graph Neural Network Explanations based on Counterfactual and Factual Reasoning Juntao Tan Shijie Geng Zuohui Fu Yingqiang Ge Shuyuan Xu Yunqi Li Yongfeng Zhang 65 111 0 17 Feb 2022
A Survey of Uncertainty in Deep Neural Networks J. Gawlikowski Cedrique Rovile Njieutcheu Tassi Mohsin Ali Jongseo Lee Matthias Humt ... R. Roscher Muhammad Shahzad Wen Yang R. Bamler Xiaoxiang Zhu BDL UQCV OOD 224 1,146 0 07 Jul 2021
δ-CLUE: Diverse Sets of Explanations for Uncertainty Estimates Dan Ley Umang Bhatt Adrian Weller UQCV 14 7 0 13 Apr 2021
Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties Lisa Schut Oscar Key R. McGrath Luca Costabello Bogdan Sacaleanu Medb Corcoran Y. Gal CML 93 48 0 16 Mar 2021
CF-GNNExplainer: Counterfactual Explanations for Graph Neural Networks Ana Lucic Maartje ter Hoeve Gabriele Tolomei Maarten de Rijke Fabrizio Silvestri 143 145 0 05 Feb 2021
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu ... Xiaochun Cao Abbas Khosravi U. Acharya V. Makarenkov S. Nahavandi BDL UQCV 312 1,914 0 12 Nov 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 79 64 0 11 Sep 2020
Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antorán Umang Bhatt T. Adel Adrian Weller José Miguel Hernández-Lobato UQCV BDL 75 116 0 11 Jun 2020
Uncertainty Quantification Using Neural Networks for Molecular Property Prediction Lior Hirschfeld Kyle Swanson Kevin Kaichuang Yang Regina Barzilay Connor W. Coley 72 191 0 20 May 2020
A Simple Baseline for Bayesian Uncertainty in Deep Learning Wesley J. Maddox T. Garipov Pavel Izmailov Dmitry Vetrov A. Wilson BDL UQCV 82 807 0 07 Feb 2019
How Powerful are Graph Neural Networks? Keyulu Xu Weihua Hu J. Leskovec Stefanie Jegelka GNN 232 7,638 0 01 Oct 2018
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 239 4,259 0 22 Jun 2017
MoleculeNet: A Benchmark for Molecular Machine Learning Zhenqin Wu Bharath Ramsundar Evan N. Feinberg Joseph Gomes C. Geniesse Aneesh S. Pappu K. Leswing Vijay S. Pande OOD 326 1,826 0 02 Mar 2017
Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf Max Welling GNN SSL 597 28,999 0 09 Sep 2016
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 65 1,092 0 16 Aug 2016