| The malicious use of artificial intelligence: Forecasting, prevention, and mitigation M Brundage, S Avin, J Clark, H Toner, P Eckersley, B Garfinkel, A Dafoe, ... arXiv preprint arXiv:1802.07228, 2018 | 1415 | 2018 |
| Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation L Kuhn, Y Gal, S Farquhar ICLR, 2022 | 387 | 2022 |
| Towards Robust Evaluations of Continual Learning S Farquhar, Y Gal arXiv preprint arXiv:1805.09733, 2018 | 341 | 2018 |
| Detecting hallucinations in large language models using semantic entropy S Farquhar, J Kossen, L Kuhn, Y Gal Nature 630 (8017), 625-630, 2024 | 214 | 2024 |
| Model evaluation for extreme risks T Shevlane, S Farquhar, B Garfinkel, M Phuong, J Whittlestone, J Leung, ... arXiv preprint arXiv:2305.15324, 2023 | 171 | 2023 |
| Prioritized Training on Points that are Learnable, Worth Learning, and not yet Learnt S Mindermann, JM Brauner, MT Razzak, M Sharma, A Kirsch, W Xu, ... International Conference on Machine Learning, 15630-15649, 2022 | 160 | 2022 |
| Benchmarking Bayesian Deep Learning with Diabetic Retinopathy Diagnosis A Filos, S Farquhar, AN Gomez, TGJ Rudner, Z Kenton, L Smith, ... Preprint, 2019 | 155* | 2019 |
| Uncertainty Baselines: Benchmarks for uncertainty & robustness in deep learning Z Nado, N Band, M Collier, J Djolonga, MW Dusenberry, S Farquhar, ... arXiv preprint arXiv:2106.04015, 2021 | 112 | 2021 |
| On Statistical Bias In Active Learning: How and When To Fix It S Farquhar, Y Gal, T Rainforth International Conference on Learning Representations, 2021 | 106 | 2021 |
| A Unifying Bayesian View of Continual Learning S Farquhar, Y Gal Bayesian Deep Learning Workshop at NeurIPS arXiv:1902.06494, 2018 | 90 | 2018 |
| Radial Bayesian Neural Networks: Robust Variational Inference In Big Models S Farquhar, M Osborne, Y Gal Proceedings of the International Conference on Artificial Intelligence and …, 2020 | 87* | 2020 |
| Tracr: Compiled transformers as a laboratory for interpretability D Lindner, J Kramár, S Farquhar, M Rahtz, T McGrath, V Mikulik arXiv preprint arXiv:2301.05062, 2023 | 70 | 2023 |
| Active Testing: Sample-Efficient Model Evaluation J Kossen, S Farquhar, Y Gal, T Rainforth International Conference on Machine Learning, 2021 | 67 | 2021 |
| Do Bayesian Neural Networks Need To Be Fully Stochastic? M Sharma, S Farquhar, E Nalisnick, T Rainforth International Conference on Artificial Intelligence and Statistics, 7694-7722, 2023 | 60 | 2023 |
| Global Catastrophic Risks O Cotton-Barratt, S Farquhar, J Halstead, S Schubert, A Snyder-Beattie Global Challenges Foundation, 2016 | 59* | 2016 |
| Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations S Farquhar, L Smith, Y Gal Advances in Neural Information Processing Systems, 2020 | 57 | 2020 |
| Evaluating Frontier Models for Dangerous Capabilities M Phuong, M Aitchison, E Catt, S Cogan, A Kaskasoli, V Krakovna, ... arXiv preprint arXiv:2403.13793, 2024 | 55 | 2024 |
| Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning A Kirsch, S Farquhar, P Atighehchian, A Jesson, F Branchaud-Charron, ... | 53* | |
| Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients M Alizadeh, SA Tailor, LM Zintgraf, J van Amersfoort, S Farquhar, ... International Conference on Learning Representations, 2022 | 51 | 2022 |
| Prediction-Oriented Bayesian Active Learning FB Smith, A Kirsch, S Farquhar, Y Gal, A Foster, T Rainforth International Conference on Artificial Intelligence and Statistics, 7331-7348, 2023 | 44* | 2023 |
Yarin GalAssociate Professor, University of OxfordE-mail confirmado em cs.ox.ac.uk