| With or without you: predictive coding and Bayesian inference in the brain L Aitchison, M Lengyel Current opinion in neurobiology 46, 219-227, 2017 | 99 | 2017 |
| Deep convolutional networks as shallow gaussian processes A Garriga-Alonso, CE Rasmussen, L Aitchison arXiv preprint arXiv:1808.05587, 2018 | 97 | 2018 |
| Doubly Bayesian analysis of confidence in perceptual decision-making L Aitchison, D Bang, B Bahrami, PE Latham PLoS Comput Biol 11 (10), e1004519, 2015 | 92 | 2015 |
| Zipf’s law arises naturally when there are underlying, unobserved variables L Aitchison, N Corradi, PE Latham PLoS computational biology 12 (12), e1005110, 2016 | 64 | 2016 |
| Confidence matching in group decision-making D Bang, L Aitchison, R Moran, SH Castanon, B Rafiee, A Mahmoodi, ... Nature Human Behaviour 1 (6), 1-7, 2017 | 47 | 2017 |
| Active dendritic integration as a mechanism for robust and precise grid cell firing C Schmidt-Hieber, G Toleikyte, L Aitchison, A Roth, BA Clark, T Branco, ... Nature neuroscience 20 (8), 1114, 2017 | 43 | 2017 |
| The Hamiltonian brain: Efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics L Aitchison, M Lengyel PLoS computational biology 12 (12), e1005186, 2016 | 40 | 2016 |
| Fast Sampling-Based Inference in Balanced Neuronal Networks. G Hennequin, L Aitchison, M Lengyel NIPS 27, 2240-2248, 2014 | 39 | 2014 |
| Probabilistic synapses L Aitchison, A Pouget, PE Latham arXiv preprint arXiv:1410.1029, 2014 | 23* | 2014 |
| Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit L Aitchison, L Russell, A Packer, J Yan, P Castonguay, M Häusser, ... Advances in Neural Information Processing Systems 2017, 3487-3496, 2017 | 13 | 2017 |
| Cortical-like dynamics in recurrent circuits optimized for sampling-based probabilistic inference R Echeveste, L Aitchison, G Hennequin, M Lengyel Nature Neuroscience 23 (9), 1138-1149, 2020 | 7 | 2020 |
| Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods L Aitchison arXiv preprint arXiv:1807.07540, 2018 | 7* | 2018 |
| Global inducing point variational posteriors for bayesian neural networks and deep gaussian processes SW Ober, L Aitchison arXiv preprint arXiv:2005.08140, 2020 | 6 | 2020 |
| Why bigger is not always better: on finite and infinite neural networks L Aitchison International Conference on Machine Learning, 156-164, 2020 | 5 | 2020 |
| A statistical theory of semi-supervised learning L Aitchison arXiv preprint arXiv:2008.05913, 2020 | 3 | 2020 |
| A statistical theory of cold posteriors in deep neural networks L Aitchison arXiv preprint arXiv:2008.05912, 2020 | 2 | 2020 |
| Discrete flow posteriors for variational inference in discrete dynamical systems L Aitchison, V Adam, SC Turaga arXiv preprint arXiv:1805.10958, 2018 | 2 | 2018 |
| Deep kernel processes L Aitchison, AX Yang, SW Ober arXiv preprint arXiv:2010.01590, 2020 | 1 | 2020 |
| A statistical theory of out-of-distribution detection X Wang, L Aitchison arXiv preprint arXiv:2102.12959, 2021 | | 2021 |
| Bayesian Neural Network Priors Revisited V Fortuin, A Garriga-Alonso, F Wenzel, G Rätsch, R Turner, ... arXiv preprint arXiv:2102.06571, 2021 | | 2021 |
