| Unsupervised learning of phase transitions: from principal component analysis to variational autoencoders SJ Wetzel arXiv preprint arXiv:1703.02435, 2017 | 513 | 2017 |
| Machine learning of explicit order parameters: From the Ising model to SU (2) lattice gauge theory SJ Wetzel, M Scherzer Physical Review B 96 (18), 184410, 2017 | 162 | 2017 |
| Discovering symmetry invariants and conserved quantities by interpreting siamese neural networks SJ Wetzel, RG Melko, J Scott, M Panju, V Ganesh Physical Review Research 2 (3), 033499, 2020 | 86 | 2020 |
| Physics and the choice of regulators in functional renormalisation group flows JM Pawlowski, MM Scherer, R Schmidt, SJ Wetzel Annals of Physics 384, 165-197, 2017 | 73 | 2017 |
| Modern applications of machine learning in quantum sciences A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint arXiv:2204.04198, 2022 | 71 | 2022 |
| Spectral reconstruction with deep neural networks L Kades, JM Pawlowski, A Rothkopf, M Scherzer, JM Urban, SJ Wetzel, ... Physical Review D 102 (9), 096001, 2020 | 64 | 2020 |
| Toward orbital-free density functional theory with small data sets and deep learning K Ryczko, SJ Wetzel, RG Melko, I Tamblyn Journal of Chemical Theory and Computation 18 (2), 1122-1128, 2022 | 35 | 2022 |
| Logic guided genetic algorithms (student abstract) D Ashok, J Scott, SJ Wetzel, M Panju, V Ganesh Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 15753 …, 2021 | 26 | 2021 |
| Twin neural network regression is a semi-supervised regression algorithm SJ Wetzel, RG Melko, I Tamblyn Machine Learning: Science and Technology 3 (4), 045007, 2022 | 13 | 2022 |
| Twin neural network regression SJ Wetzel, K Ryczko, RG Melko, I Tamblyn Applied AI Letters 3 (4), e78, 2022 | 12 | 2022 |
| Unsupervised learning of Rydberg atom array phase diagram with Siamese neural networks Z Patel, E Merali, SJ Wetzel New Journal of Physics 24 (11), 113021, 2022 | 9 | 2022 |
| Modern applications of machine learning in quantum sciences. 2022. doi: 10.48550 A Dawid, J Arnold, B Requena, A Gresch, M Płodzień, K Donatella, ... arXiv preprint ARXIV.2204.04198, 0 | 8 | |
| Closed-Form Interpretation of Neural Network Classifiers with Symbolic Regression Gradients SJ Wetzel arXiv preprint arXiv:2401.04978, 2024 | 3 | 2024 |
| Exploring the hubbard model on the square lattice at zero temperature with a bosonized functional renormalization approach SJ Wetzel arXiv preprint arXiv:1712.04297, 2017 | 3 | 2017 |
| Twin neural network improved k-nearest neighbor regression SJ Wetzel International Journal of Data Science and Analytics, 1-11, 2024 | 1 | 2024 |
| How to get the most out of Twinned Regression Methods SJ Wetzel arXiv preprint arXiv:2301.01383, 2023 | 1 | 2023 |
| Exploring Phase Diagrams with Functional Renormalization and Artificial Neural Networks: From the Hubbard Model to Lattice Gauge Theory SJ Wetzel | 1 | 2018 |
| Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients Z Patel, SJ Wetzel arXiv preprint arXiv:2409.05305, 2024 | | 2024 |