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