| Bayesian renormalization DS Berman, MS Klinger, AG Stapleton Machine Learning: Science and Technology 4 (4), 045011, 2023 | 34 | 2023 |
| Bootstrability in line-defect CFTs with improved truncation methods V Niarchos, C Papageorgakis, P Richmond, AG Stapleton, M Woolley Physical Review D 108 (10), 105027, 2023 | 25 | 2023 |
| NCoder—a quantum field theory approach to encoding data DS Berman, MS Klinger, AG Stapleton Machine Learning: Science and Technology 6 (2), 025059, 2025 | 7 | 2025 |
| Bayesian RG flow in neural network field theories JN Howard, M Klinger, A Maiti, AG Stapleton SciPost Physics Core 8 (1), 027, 2025 | 6 | 2025 |
| Grokking vs. Learning: Same features, different encodings D Manning-Coe, J Gliozzi, AG Stapleton, E Hirst, G De Tomasi, B Bradlyn, ... arXiv preprint arXiv:2502.01739, 2025 | 2 | 2025 |
| A path to natural language through tokenisation and transformers DS Berman, AG Stapleton arXiv preprint arXiv:2601.03368, 2026 | | 2026 |
| Same features, different encodings: three case studies of path dependence in grokking and learning. D Manning-Coe, J Gliozzi, AG Stapleton, E Hirst, M Klinger, G de Tomasi, ... SMT 2025, 2025 | | 2025 |
| AInstein: Numerical Einstein Metrics via Machine Learning E Hirst, TS Gherardini, AG Stapleton arXiv preprint arXiv:2502.13043, 2025 | | 2025 |
David BermanQueen Mary University of LondonVerified email at qmul.ac.uk
