Laurence Aitchison
Title
Cited by
Cited by
Year
With or without you: predictive coding and Bayesian inference in the brain
L Aitchison, M Lengyel
Current opinion in neurobiology 46, 219-227, 2017
992017
Deep convolutional networks as shallow gaussian processes
A Garriga-Alonso, CE Rasmussen, L Aitchison
arXiv preprint arXiv:1808.05587, 2018
972018
Doubly Bayesian analysis of confidence in perceptual decision-making
L Aitchison, D Bang, B Bahrami, PE Latham
PLoS Comput Biol 11 (10), e1004519, 2015
922015
Zipf’s law arises naturally when there are underlying, unobserved variables
L Aitchison, N Corradi, PE Latham
PLoS computational biology 12 (12), e1005110, 2016
642016
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
472017
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
432017
The Hamiltonian brain: Efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics
L Aitchison, M Lengyel
PLoS computational biology 12 (12), e1005186, 2016
402016
Fast Sampling-Based Inference in Balanced Neuronal Networks.
G Hennequin, L Aitchison, M Lengyel
NIPS 27, 2240-2248, 2014
392014
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
132017
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
72020
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
62020
Why bigger is not always better: on finite and infinite neural networks
L Aitchison
International Conference on Machine Learning, 156-164, 2020
52020
A statistical theory of semi-supervised learning
L Aitchison
arXiv preprint arXiv:2008.05913, 2020
32020
A statistical theory of cold posteriors in deep neural networks
L Aitchison
arXiv preprint arXiv:2008.05912, 2020
22020
Discrete flow posteriors for variational inference in discrete dynamical systems
L Aitchison, V Adam, SC Turaga
arXiv preprint arXiv:1805.10958, 2018
22018
Deep kernel processes
L Aitchison, AX Yang, SW Ober
arXiv preprint arXiv:2010.01590, 2020
12020
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
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