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 | 981 | 2018 |
Towards Robust Evaluations of Continual Learning S Farquhar, Y Gal arXiv preprint arXiv:1805.09733, 2018 | 289 | 2018 |
Benchmarking Bayesian Deep Learning with Diabetic Retinopathy Diagnosis A Filos, S Farquhar, AN Gomez, TGJ Rudner, Z Kenton, L Smith, ... Preprint, 2019 | 128* | 2019 |
Semantic Uncertainty: Linguistic Invariances for Uncertainty Estimation in Natural Language Generation L Kuhn, Y Gal, S Farquhar ICLR, 2022 | 118 | 2022 |
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 | 97 | 2021 |
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 | 81 | 2022 |
On Statistical Bias In Active Learning: How and When To Fix It S Farquhar, Y Gal, T Rainforth International Conference on Learning Representations, 2021 | 80 | 2021 |
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 | 73* | 2020 |
A Unifying Bayesian View of Continual Learning S Farquhar, Y Gal Bayesian Deep Learning Workshop at NeurIPS arXiv:1902.06494, 2018 | 72 | 2018 |
Model evaluation for extreme risks T Shevlane, S Farquhar, B Garfinkel, M Phuong, J Whittlestone, J Leung, ... arXiv preprint arXiv:2305.15324, 2023 | 68 | 2023 |
Global Catastrophic Risks O Cotton-Barratt, S Farquhar, J Halstead, S Schubert, A Snyder-Beattie Global Challenges Foundation, 2016 | 56* | 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 | 48 | 2020 |
Active Testing: Sample-Efficient Model Evaluation J Kossen, S Farquhar, Y Gal, T Rainforth International Conference on Machine Learning, 2021 | 43 | 2021 |
Existential Risk: Diplomacy and Governance S Farquhar, J Halstead, O Cotton-Barratt, S Schubert, H Belfield, ... Global Priorities Project, 2017 | 36* | 2017 |
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 | 27 | 2023 |
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 | 27 | 2022 |
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning A Kirsch, S Farquhar, P Atighehchian, A Jesson, F Branchaud-Charron, ... | 24* | |
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 | 19 | 2023 |
Single Shot Structured Pruning Before Training J van Amersfoort, M Alizadeh, S Farquhar, N Lane, Y Gal arXiv preprint arXiv:2007.00389, 2020 | 19 | 2020 |
CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models L Kuhn, Y Gal, S Farquhar arXiv preprint arXiv:2212.07769, 2022 | 16* | 2022 |