| Bayesian reasoning and machine learning D Barber Cambridge University Press, 2012 | 2780 | 2012 |
| Bayesian classification with Gaussian processes CKI Williams, D Barber IEEE Transactions on pattern analysis and machine intelligence 20 (12), 1342 …, 1998 | 1159 | 1998 |
| The IM algorithm: a variational approach to information maximization DBF Agakov Advances in neural information processing systems 16 (320), 201, 2004 | 616* | 2004 |
| A scalable laplace approximation for neural networks H Ritter, A Botev, D Barber 6th international conference on learning representations, ICLR 2018 …, 2018 | 611 | 2018 |
| Thinking fast and slow with deep learning and tree search T Anthony, Z Tian, D Barber Advances in neural information processing systems 30, 2017 | 547 | 2017 |
| Bayesian time series models D Barber, AT Cemgil, S Chiappa Cambridge University Press, 2011 | 445 | 2011 |
| Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting H Ritter, A Botev, D Barber Neural Information Processing Systems, 2018 | 441 | 2018 |
| Ensemble learning in Bayesian neural networks D Barber, CM Bishop Nato ASI Series F Computer and Systems Sciences 168, 215-238, 1998 | 330 | 1998 |
| Practical Gauss-Newton optimisation for deep learning A Botev, H Ritter, D Barber International Conference on Machine Learning, 557-565, 2017 | 326 | 2017 |
| Optimal spike-timing-dependent plasticity for precise action potential firing in supervised learning JP Pfister, T Toyoizumi, D Barber, W Gerstner Neural computation 18 (6), 1318-1348, 2006 | 323 | 2006 |
| Nesterov's accelerated gradient and momentum as approximations to regularised update descent A Botev, G Lever, D Barber 2017 International joint conference on neural networks (IJCNN), 1899-1903, 2017 | 235 | 2017 |
| Practical Lossless Compression with Latent Variables using Bits Back Coding james townsend | 188 | 2019 |
| A generative model for music transcription AT Cemgil, HJ Kappen, D Barber IEEE Transactions on Audio, Speech, and Language Processing 14 (2), 679-694, 2006 | 187 | 2006 |
| Towards algorithm auditing: managing legal, ethical and technological risks of AI, ML and associated algorithms A Koshiyama, E Kazim, P Treleaven, P Rai, L Szpruch, G Pavey, ... Royal Society Open Science 11 (5), 230859, 2024 | 174 | 2024 |
| Computed tomographic biomarkers in idiopathic pulmonary fibrosis. The future of quantitative analysis X Wu, GH Kim, ML Salisbury, D Barber, BJ Bartholmai, KK Brown, ... American journal of respiratory and critical care medicine 199 (1), 12-21, 2019 | 174 | 2019 |
| Modular networks: Learning to decompose neural computation L Kirsch, J Kunze, D Barber Advances in neural information processing systems 31, 2018 | 166 | 2018 |
| Ensemble learning for multi-layer networks D Barber, C Bishop Advances in neural information processing systems 10, 1997 | 133 | 1997 |
| On the computational complexity of stochastic controller optimization in POMDPs N Vlassis, ML Littman, D Barber ACM Transactions on Computation Theory (TOCT) 4 (4), 1-8, 2012 | 123 | 2012 |
| Gaussian processes for Bayesian classification via hybrid Monte Carlo D Barber, C Williams Advances in neural information processing systems 9, 1996 | 117 | 1996 |
| Expectation correction for smoothed inference in switching linear dynamical systems. D Barber Journal of Machine Learning Research 7 (11), 2006 | 116 | 2006 |
Mingtian ZhangResearch Fellow, University College LondonVerified email at ucl.ac.uk
