Amanda S. Barnard

According to our database, Amanda S. Barnard authored at least 19 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2024

Classification of battery compounds using structure-free Mendeleev encodings.

Zixin Zhuang, Amanda S. Barnard

J. Cheminformatics, December, 2024

Online meta-learned gradient norms for active learning in science and technology.

Haiqi Dong, Amanda S. Barnard, Amanda J. Parker

Mach. Learn. Sci. Technol., March, 2024

EXAGREE: Towards Explanation Agreement in Explainable Machine Learning.

Sichao Li, Quanling Deng, Amanda S. Barnard

CoRR, 2024

Practical Attribution Guidance for Rashomon Sets.

Sichao Li, Amanda S. Barnard, Quanling Deng

CoRR, 2024

Diverse Explanations from Data-driven and Domain-driven Perspectives for Machine Learning Models.

Sichao Li, Amanda S. Barnard

CoRR, 2024

Exploring the cloud of feature interaction scores in a Rashomon set.

Sichao Li, Rong Wang, Quanling Deng, Amanda S. Barnard

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis.

Weitong Huang, Hanna Suominen, Tommy Liu, Gregory Rice, Carlos Salomon, Amanda S. Barnard

J. Biomed. Informatics, May, 2023

A Machine Learning Approach Towards Runtime Optimisation of Matrix Multiplication.

Yufan Xia, Marco De La Pierre, Amanda S. Barnard, Giuseppe M. J. Barca

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

Variance Tolerance Factors For Interpreting All Neural Networks.

Sichao Li, Amanda S. Barnard

Proceedings of the International Joint Conference on Neural Networks, 2023

Shapley Based Residual Decomposition for Instance Analysis.

Tommy Liu, Amanda S. Barnard

Proceedings of the International Conference on Machine Learning, 2023

2022

Federated data processing and learning for collaboration in the physical sciences.

W. Huang, Amanda S. Barnard

Mach. Learn. Sci. Technol., December, 2022

The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts.

Sichao Li, Jonathan Y. C. Ting, Amanda S. Barnard

J. Comput. Sci., 2022

Variance Tolerance Factors For Interpreting Neural Networks.

Sichao Li, Amanda S. Barnard

CoRR, 2022

Optimization-Free Inverse Design of High-Dimensional Nanoparticle Electrocatalysts Using Multi-target Machine Learning.

Sichao Li, Jonathan Y. C. Ting, Amanda S. Barnard

Proceedings of the Computational Science - ICCS 2022, 2022

2021

Fast derivation of Shapley based feature importances through feature extraction methods for nanoinformatics.

Tommy Liu, Amanda S. Barnard

Mach. Learn. Sci. Technol., 2021

2017

Machine Learning for Silver Nanoparticle Electron Transfer Property Prediction.

Baichuan Sun, Michael Fernández, Amanda S. Barnard

J. Chem. Inf. Model., October, 2017

On reverse Monte Carlo constraints and model reproduction.

George Opletal, Timothy C. Petersen, Amanda S. Barnard, Salvy P. Russo

J. Comput. Chem., 2017

2015

Quantitative Structure-Property Relationship Modeling of Electronic Properties of Graphene Using Atomic Radial Distribution Function Scores.

Michael Fernández, Hongqing Shi, Amanda S. Barnard

J. Chem. Inf. Model., 2015

2011

Useful equations for modeling the relative stability of common nanoparticle morphologies.

Amanda S. Barnard

Comput. Phys. Commun., 2011