| Non-markovian reward modelling from trajectory labels via interpretable multiple instance learning J Early, T Bewley, C Evers, S Ramchurn Advances in Neural Information Processing Systems 35, 27652-27663, 2022 | 12 | 2022 |
| Model Agnostic Interpretability for Multiple Instance Learning J Early, C Evers, S Ramchurn International Conference on Learning Representations, 2022 | 7 | 2022 |
| Inherently Interpretable Time Series Classification via Multiple Instance Learning J Early, GKC Cheung, K Cutajar, H Xie, J Kandola, N Twomey arXiv preprint arXiv:2311.10049, 2023 | 4 | 2023 |
| Non-Asimov Explanations Regulating AI through Transparency C Reed, K Grieman, J Early arXiv preprint arXiv:2111.13041, 2021 | 4 | 2021 |
| Inferring Player Location in Sports Matches: Multi-Agent Spatial Imputation from Limited Observations G Everett, RJ Beal, T Matthews, J Early, TJ Norman, SD Ramchurn arXiv preprint arXiv:2302.06569, 2023 | 3 | 2023 |
| Scene-to-Patch Earth Observation: Multiple Instance Learning for Land Cover Classification J Early, YJ Deweese, C Evers, S Ramchurn arXiv preprint arXiv:2211.08247, 2022 | 3 | 2022 |
| Reducing Catastrophic Forgetting when Evolving Neural Networks J Early arXiv preprint arXiv:1904.03178, 2019 | 3 | 2019 |
| Predicting response to neoadjuvant therapy using image capture from diagnostic biopsies of oesophageal adenocarcinoma S Rahman, J Early, M De Vries, M Lloyd, B Grace, G Ramchurn, ... European Journal of Surgical Oncology 47 (1), e4, 2020 | 2 | 2020 |
| Extending scene-to-patch models: Multi-resolution multiple instance learning for Earth observation J Early, YJC Deweese, C Evers, S Ramchurn Environmental Data Science 2, e42, 2023 | 1 | 2023 |
| A Risk-Based Approach to AI Regulation: System Categorisation and Explainable AI Practices K Grieman, J Early SCRIPTed 20, 56, 2023 | 1 | 2023 |
| Revisiting Deep Fisher Vectors: Using Fisher Information to Improve Object Classification S Ahmed, T Azim, J Early, SD Ramchurn | 1 | 2022 |
| Predicting survival and response to therapy using diagnostic biopsies: A machine learning approach to facilitate treatment decisions for oesophageal adenocarcinoma S Rahman, J Early, B Sharpe, M Lloyd, MD Vries, B Grace, S Ramchurn, ... British Journal of Surgery 108 (Supplement_9), znab430. 185, 2021 | 1 | 2021 |
| Interpretable multiple instance learning JA Early University of Southampton, 2024 | | 2024 |
| Digital Pathology and Machine Learning for Prediction of Response to Neoadjuvant Chemotherapy in Oesophageal Adenocarcinoma B Sharpe, S Rahman, J Early, G Vigneswaran, J Horne, B Grace, J West, ... Journal of Pathology 255, S35-S35, 2021 | | 2021 |
| Neural network image capture to predict response of oesophageal adenocarcinoma to neoadjuvant therapy S Rahman, J Early, B Sharpe, M Lloyd, B Grace, M De Vries, S Ramchurn, ... British Journal of Surgery 108 (Supplement_5), 2021 | | 2021 |
Sarvapali D. (Gopal) RamchurnProfessor of Artificial Intelligence, University of Southampton在 ecs.soton.ac.uk 的电子邮件经过验证
