Fetching the paper…
Reading the bibliography…
Explainable AI (XAI) is a research area whose objective is to increase trustworthiness and to enlighten the hidden mechanism of opaque machine learning techniques.
Reinforcement learning: an introduction cambridge
Richard S Sutton and Andrew G Barto · 1998
Earlier work this paper cites.
Rdkit: Open-source cheminformatics, 2006
Greg Landrum et al · 2006
Earlier work this paper cites.
An introduction to recursive neural networks and kernel methods for cheminformatics
Alessio Micheli, Alessandro Sperduti, and Antonina Starita · 2007
Earlier work this paper cites.
Neural network for graphs: A contextual constructive approach
A. Micheli · 2009
Earlier work this paper cites.
Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
Earlier work this paper cites.
Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
Earlier work this paper cites.
Vqa: Visual question answering, 2015
A. Agrawal, J. Lu, S. Antol, M. Mitchell, C. Lawrence Zitnick, D. Batra, and D. Parikh · 2015
Earlier work this paper cites.
Learning deep features for discriminative localization, 2015
Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba · 2015
Earlier work this paper cites.
Multiobjective reinforcement learning: A comprehensive overview
C. Liu, X. Xu, and D. Hu · 2015
Earlier work this paper cites.
Deep reinforcement learning with double q-learning, 2015
Hado van Hasselt, Arthur Guez, and David Silver · 2015
Earlier work this paper cites.
”why should I trust you?”: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
Earlier work this paper cites.
Benchmark data sets for graph kernels, 2016
Kristian Kersting, Nils M. Kriege, Christopher Morris, Petra Mutzel, and Marion Neumann · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks, 2017
Thomas N. Kipf and Max Welling · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs
William L. Hamilton, Rex Ying, and Jure Leskovec · 2017
Cited alongside, same era.
Matching node embeddings for graph similarity
Giannis Nikolentzos, Polykarpos Meladianos, and Michalis Vazirgiannis · 2017
Cited alongside, same era.
Optimizing distributions over molecular space. an objective-reinforced generative adversarial network for inverse-design chemistry (organic), Aug 2017
Benjamin Sanchez-Lengeling, Carlos Outeiral, Gabriel L. Guimaraes, and Alan Aspuru-Guzik · 2017
Cited alongside, same era.
Moleculenet: A benchmark for molecular machine learning, 2017
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande · 2017
Cited alongside, same era.
Graph neural networks: A review of methods and applications, 2018
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun · 2018
Cited alongside, same era.
A gentle introduction to deep learning for graphs
Davide Bacciu, Federico Errica, Alessio Micheli, and Marco Podda · 2020
Later among the works it cites.
A fair comparison of graph neural networks for graph classification
Federico Errica, Marco Podda, Davide Bacciu, and Alessio Micheli · 2020
Later among the works it cites.
Fast real-time counterfactual explanations, 2020
Yunxia Zhao · 2020
Later among the works it cites.
Xgnn: Towards model-level explanations of graph neural networks
Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji · 2020
Later among the works it cites.
Probabilistic learning on graphs via contextual architectures
Davide Bacciu, Federico Errica, and Alessio Micheli · 2020
Later among the works it cites.
Interpretable counterfactual explanations guided by prototypes, 2020
Arnaud Van Looveren and Janis Klaise · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Counterfactual explanations without opening the black box: Automated decisions and the gdpr, 2018
Sandra Wachter, Brent Mittelstadt, and Chris Russell · 2018
Cited alongside, same era.
Deep reinforcement learning for de novo drug design
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 2018
Cited alongside, same era.
Optimization of molecules via deep reinforcement learning, 2018
Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare, and Patrick Riley · 2018
Cited alongside, same era.
Weisfeiler and leman go neural: Higher-order graph neural networks, 2018
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2018
Cited alongside, same era.
Societal issues in machine learning: When learning from data is not enough
Davide Bacciu, Battista Biggio, Paulo Lisboa, José D. Martín, Luca Oneto, and Alfredo Vellido · 2019
Cited alongside, same era.
Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour · 2019
Cited alongside, same era.
Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 2019
Cited alongside, same era.
Relex: A model-agnostic relational model explainer, 2020
Yue Zhang, David Defazio, and Arti Ramesh · 2020
Later among the works it cites.
Graphlime: Local interpretable model explanations for graph neural networks, 2020
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, Dawei Yin, and Yi Chang · 2020
Later among the works it cites.
Contrastive graph neural network explanation, 2020
Lukas Faber, Amin K. Moghaddam, and Roger Wattenhofer · 2020
Later among the works it cites.
Interpretable machine learning for perturbation biology
Bo Yuan, Ciyue Shen, Augustin Luna, Anil Korkut, Debora S Marks, John Ingraham, and Chris Sander · 2020
Later among the works it cites.
Opening the black box: Interpretable machine learning for geneticists
Christina B. Azodi, Jiliang Tang, and Shin-Han Shiu · 2020
Later among the works it cites.
Edge-based sequential graph generation with recurrent neural networks
Davide Bacciu, Alessio Micheli, and Marco Podda · 2020
Later among the works it cites.
A deep generative model for fragment-based molecule generation
Marco Podda, Davide Bacciu, and Alessio Micheli · 2020
Later among the works it cites.