| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 1290* | 2023 |
| Meta-learning probabilistic inference for prediction J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner arXiv preprint arXiv:1805.09921, 2018 | 291 | 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 | 244 | 2019 |
| Convolutional conditional neural processes J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner arXiv preprint arXiv:1910.13556, 2019 | 148 | 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 | 133 | 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 | 106 | 2020 |
| Permutation equivariant models for compositional generalization in language J Gordon, D Lopez-Paz, M Baroni, D Bouchacourt International Conference on Learning Representations, 2019 | 103 | 2019 |
| Probabilistic neural architecture search FP Casale, J Gordon, N Fusi arXiv preprint arXiv:1902.05116, 2019 | 81 | 2019 |
| 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 | 59 | 2020 |
| Combining deep generative and discriminative models for Bayesian semi-supervised learning J Gordon, JM Hernández-Lobato Pattern Recognition 100, 107156, 2020 | 55 | 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 | 41 | 2023 |
| Bayesian semisupervised learning with deep generative models J Gordon, JM Hernández-Lobato arXiv preprint arXiv:1706.09751, 2017 | 34 | 2017 |
| The Gaussian neural process WP Bruinsma, J Requeima, AYK Foong, J Gordon, RE Turner arXiv preprint arXiv:2101.03606, 2021 | 32 | 2021 |
| Insights into amyotrophic lateral sclerosis from a machine learning perspective J Gordon, B Lerner Journal of Clinical Medicine 8 (10), 1578, 2019 | 29 | 2019 |
| Neural process family Y Dubois, J Gordon, AY Foong Neural-Process-Family 2 (6), 8, 2020 | 27 | 2020 |
| Predictive complexity priors E Nalisnick, J Gordon, JM Hernández-Lobato International Conference on Artificial Intelligence and Statistics, 694-702, 2021 | 23 | 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 | 15 | 2018 |
| Refining the variational posterior through iterative optimization M Havasi, J Snoek, D Tran, J Gordon, JM Hernández-Lobato | 6 | 2019 |
| Advances in Probabilistic Meta-Learning and the Neural Process Family J Gordon | 5 | 2021 |
| Consolidating the Meta-Learning Zoo: A Unifying Perspective as Posterior Predictive Inference J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner Workshop on Meta-Learning (MetaLearn 2018) at the 32nd Conference on Neural …, 2018 | 2 | 2018 |
Richard E TurnerProfessor, University of CambridgeПідтверджена електронна адреса в cam.ac.uk
