| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 9757* | 2023 |
| Meta-learning probabilistic inference for prediction J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner arXiv preprint arXiv:1805.09921, 2018 | 333 | 2018 |
| Fast and flexible multi-task classification using conditional neural adaptive processes J Requeima, J Gordon, J Bronskill, S Nowozin, RE Turner Advances in neural information processing systems 32, 2019 | 295 | 2019 |
| Convolutional conditional neural processes J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner arXiv preprint arXiv:1910.13556, 2019 | 182 | 2019 |
| Bayesian batch active learning as sparse subset approximation R Pinsler, J Gordon, E Nalisnick, JM Hernández-Lobato Advances in neural information processing systems 32, 2019 | 148 | 2019 |
| Permutation equivariant models for compositional generalization in language J Gordon, D Lopez-Paz, M Baroni, D Bouchacourt International Conference on Learning Representations, 2019 | 121 | 2019 |
| Tasknorm: Rethinking batch normalization for meta-learning J Bronskill, J Gordon, J Requeima, S Nowozin, R Turner International Conference on Machine Learning, 1153-1164, 2020 | 120 | 2020 |
| Evolution through large models J Lehman, J Gordon, S Jain, K Ndousse, C Yeh, KO Stanley Handbook of Evolutionary Machine Learning, 331-366, 2023 | 103 | 2023 |
| Probabilistic neural architecture search FP Casale, J Gordon, N Fusi arXiv preprint arXiv:1902.05116, 2019 | 89 | 2019 |
| Gpt-4 technical report, 2024 JA OpenAI, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, ... URL https://arxiv. org/abs/2303.08774 2, 6, 2024 | 88 | 2024 |
| Meta-learning stationary stochastic process prediction with convolutional neural processes A Foong, W Bruinsma, J Gordon, Y Dubois, J Requeima, R Turner Advances in Neural Information Processing Systems 33, 8284-8295, 2020 | 77 | 2020 |
| Combining deep generative and discriminative models for Bayesian semi-supervised learning J Gordon, JM Hernández-Lobato Pattern Recognition 100, 107156, 2020 | 66 | 2020 |
| GPT-4 technical report (2023) J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... URL https://api. semanticscholar. org/CorpusID 257532815, 2023 | 62 | 2023 |
| Openai o1 system card A Jaech, A Kalai, A Lerer, A Richardson, A El-Kishky, A Low, A Helyar, ... arXiv preprint arXiv:2412.16720, 2024 | 45 | 2024 |
| Insights into amyotrophic lateral sclerosis from a machine learning perspective J Gordon, B Lerner Journal of Clinical Medicine 8 (10), 1578, 2019 | 38 | 2019 |
| The Gaussian neural process WP Bruinsma, J Requeima, AYK Foong, J Gordon, RE Turner arXiv preprint arXiv:2101.03606, 2021 | 36 | 2021 |
| Bayesian semisupervised learning with deep generative models J Gordon, JM Hernández-Lobato arXiv preprint arXiv:1706.09751, 2017 | 34 | 2017 |
| Predictive complexity priors E Nalisnick, J Gordon, JM Hernández-Lobato International Conference on Artificial Intelligence and Statistics, 694-702, 2021 | 25 | 2021 |
| Versa: Versatile and efficient few-shot learning J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner Third workshop on Bayesian Deep Learning, 2018 | 17 | 2018 |
| Refining the variational posterior through iterative optimization M Havasi, J Snoek, D Tran, J Gordon, JM Hernández-Lobato | 8 | 2021 |
Richard E TurnerProfessor, University of CambridgeDirección de correo verificada de cam.ac.uk
