Explainable Machine Learning for Scientific Insights and Discoveries

Explainable Machine Learning for Scientific Insights and Discoveries

21 May 2019

Marco F. Duarte

Papers citing "Explainable Machine Learning for Scientific Insights and Discoveries"

17 / 67 papers shown

Title
Applications of physics-informed scientific machine learning in subsurface science: A survey A. Sun H. Yoon C. Shih Zhi Zhong AI4CE 29 9 0 10 Apr 2021
On gray-box modeling for virtual flow metering M. Hotvedt B. Grimstad D. Ljungquist Lars Imsland 30 12 0 23 Mar 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 34 126 0 24 Jan 2021
Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges Kashif Ahmad Majdi Maabreh M. Ghaly Khalil Khan Junaid Qadir Ala I. Al-Fuqaha 27 142 0 14 Dec 2020
Explanation from Specification Harish Naik Gyorgy Turán XAI 27 0 0 13 Dec 2020
Scientific intuition inspired by machine learning generated hypotheses Pascal Friederich Mario Krenn Isaac Tamblyn Alán Aspuru-Guzik AI4CE 24 34 0 27 Oct 2020
Explainable Empirical Risk Minimization Linli Zhang Georgios Karakasidis Arina Odnoblyudova Leyla Dogruel Alex Jung 21 5 0 03 Sep 2020
The Grammar of Interactive Explanatory Model Analysis Hubert Baniecki Dariusz Parzych P. Biecek 21 44 0 01 May 2020
Neural Additive Models: Interpretable Machine Learning with Neural Nets Rishabh Agarwal Levi Melnick Nicholas Frosst Xuezhou Zhang Ben Lengerich R. Caruana Geoffrey E. Hinton 46 405 0 29 Apr 2020
Autonomous discovery in the chemical sciences part II: Outlook Connor W. Coley Natalie S. Eyke K. Jensen 29 171 0 30 Mar 2020
Computer-inspired Quantum Experiments Mario Krenn Manuel Erhard A. Zeilinger 27 73 0 23 Feb 2020
Interpretation and Simplification of Deep Forest Sangwon Kim Mira Jeong ByoungChul Ko FAtt 19 8 0 14 Jan 2020
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems Laura von Rueden S. Mayer Katharina Beckh B. Georgiev Sven Giesselbach ... Rajkumar Ramamurthy Michal Walczak Jochen Garcke Christian Bauckhage Jannis Schuecker 34 626 0 29 Mar 2019
Machine learning topological phases in real space N. L. Holanda M. Griffith 11 26 0 07 Jan 2019
Design by adaptive sampling David H. Brookes Jennifer Listgarten TPM 39 65 0 08 Oct 2018
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 234 2,238 0 24 Jun 2017
A Compositional Object-Based Approach to Learning Physical Dynamics Michael Chang T. Ullman Antonio Torralba J. Tenenbaum AI4CE OCL 241 438 0 01 Dec 2016