Laurence Aitchison

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All published works

Action	Title	Year	Authors
+ PDF Chat	Why you don't overfit, and don't need Bayes if you only train for one epoch	2024	Laurence Aitchison
+ PDF Chat	Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines	2024	Edward Milsom Ben Anson Laurence Aitchison
+ PDF Chat	Residual Stream Analysis with Multi-Layer SAEs	2024	T.V. Lawson Lucy Farnik Conor Houghton Laurence Aitchison
+	Signatures of Bayesian inference emerge from energy-efficient synapses	2024	James Malkin Cian O’Donnell Conor Houghton Laurence Aitchison
+ PDF Chat	Questionable practices in machine learning	2024	Gavin Leech Juan José Vázquez-Alcaraz Misha Yagudin Niclas Kupper Laurence Aitchison
+ PDF Chat	Using Neural Networks for Data Cleaning in Weather Datasets	2024	Jack R. P. Hanslope Laurence Aitchison
+	Signatures of Bayesian inference emerge from energy efficient synapses	2024	James Malkin Cian O’Donnell Conor Houghton Laurence Aitchison
+ PDF Chat	How to set AdamW's weight decay as you scale model and dataset size	2024	Xi Wang Laurence Aitchison
+ PDF Chat	TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction Using Vision-Based Tactile Sensing	2024	Mauro Comi Yijiong Lin Alex Church Alessio Tonioni Laurence Aitchison Nathan F. Lepora
+ PDF Chat	Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces	2024	Mauro Comi Alessio Tonioni Max Yang Jonathan Tremblay Valts Blukis Yijiong Lin Nathan F. Lepora Laurence Aitchison
+ PDF Chat	Bayesian Reward Models for LLM Alignment	2024	Adam X. Yang Maxime Robeyns Thomas Coste Jun Wang Haitham Bou Ammar Laurence Aitchison
+ PDF Chat	Flexible infinite-width graph convolutional networks and the importance of representation learning	2024	Ben Anson Edward Milsom Laurence Aitchison
+ PDF Chat	Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI	2024	Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David B. Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig Aliaksandr Hubin
+ PDF Chat	Signatures of Bayesian inference emerge from energy-efficient synapses	2023	James Malkin Cian O’Donnell Conor Houghton Laurence Aitchison
+	Signatures of Bayesian inference emerge from energy efficient synapses	2023	James Malkin Cian O’Donnell Conor Houghton Laurence Aitchison
+	Imitating careful experts to avoid catastrophic events	2023	Jack R. P. Hanslope Laurence Aitchison
+	Decision trees compensate for model misspecification	2023	Hugh Panton Gavin Leech Laurence Aitchison
+	MONGOOSE: Path-wise Smooth Bayesian Optimisation via Meta-learning	2023	Adam X. Yang Laurence Aitchison Henry B. Moss
+	Taylor TD-learning	2023	Michele Garibbo Maxime Robeyns Laurence Aitchison
+	Massively Parallel Reweighted Wake-Sleep	2023	Thomas Heap Gavin Leech Laurence Aitchison
+	An Improved Variational Approximate Posterior for the Deep Wishart Process	2023	Sebastian W. Ober Ben Anson Edward Milsom Laurence Aitchison
+	Bayesian low-rank adaptation for large language models	2023	Adam X. Yang Maxime Robeyns Xi Wang Laurence Aitchison
+	Signatures of Bayesian inference emerge from energy efficient synapses	2023	James Malkin Cian O’Donnell Conor Houghton Laurence Aitchison
+	Convolutional Deep Kernel Machines	2023	Edward Milsom Ben Anson Laurence Aitchison
+	LoRA ensembles for large language model fine-tuning	2023	Xi Wang Laurence Aitchison Maja Rudolph
+	Using autodiff to estimate posterior moments, marginals and samples	2023	S.M. Bowyer Thomas Heap Laurence Aitchison
+	The Fractions Skill Score doesn't always measure skill	2023	Bobby Antonio Laurence Aitchison
+	TouchSDF: A DeepSDF Approach for 3D Shape Reconstruction using Vision-Based Tactile Sensing	2023	Mauro Comi Yijiong Lin Alex Church Alessio Tonioni Laurence Aitchison Nathan F. Lepora
+	Mask wearing in community settings reduces SARS-CoV-2 transmission	2022	Gavin Leech Darren Smith Joshua Teperowski Monrad Jonas B. Sandbrink Benedict Snodin Robert Zinkov Benjamin Rader John S. Brownstein Yarin Gal Samir Bhatt
+	What deep reinforcement learning tells us about human motor learning and vice-versa	2022	Michele Garibbo Casimir J. H. Ludwig Nathan F. Lepora Laurence Aitchison
+	Random initialisations performing above chance and how to find them	2022	Frederik Benzing Simon Schug R. R. Meier Johannes von Oswald Yassir Akram Nicolas Zucchet Laurence Aitchison Angelika Steger
+	Robustness to corruption in pre-trained Bayesian neural networks	2022	Xi Wang Laurence Aitchison
+	Machine learning emulation of a local-scale UK climate model	2022	Henry Addison Elizabeth Kendon Suman Ravuri Laurence Aitchison P.A. Watson
+	A variational approximate posterior for the deep Wishart process	2021	Sebastian W. Ober Laurence Aitchison
+	Semi-supervised learning objectives as log-likelihoods in a generative model of data curation.	2021	Stoil Ganev Laurence Aitchison
+	Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe	2021	Mrinank Sharma Sören Mindermann Darren Smith Gavin Leech Benedict Snodin Janvi Ahuja Jonas B. Sandbrink Joshua Teperowski Monrad George Altman Gurpreet Dhaliwal
+	A fast point solver for deep nonlinear function approximators.	2021	Laurence Aitchison
+	Deep kernel machines and fast solvers for deep kernel machines	2021	Laurence Aitchison
+	A variational approximate posterior for the deep Wishart process	2021	Sebastian W. Ober Laurence Aitchison
+	InfoNCE is a variational autoencoder.	2021	Laurence Aitchison
+	Mass mask-wearing notably reduces COVID-19 transmission	2021	Gavin Leech Darren Smith Jonas B. Sandbrink Benedict Snodin Robert Zinkov Benjamin Rader John S. Brownstein Yarin Gal Samir Bhatt Mrinank Sharma
+	Data augmentation in Bayesian neural networks and the cold posterior effect.	2021	Seth Nabarro Stoil Ganev Adrià Garriga-Alonso Vincent Fortuin Mark van der Wilk Laurence Aitchison
+ PDF Chat	BNNpriors: A library for Bayesian neural network inference with different prior distributions	2021	Vincent Fortuin Adrià Garriga-Alonso Mark van der Wilk Laurence Aitchison
+	Deep kernel processes	2021	Laurence Aitchison Adam X. Yang Sebastian W. Ober
+	Synaptic plasticity as Bayesian inference	2021	Laurence Aitchison Jannes Jegminat Jorge Aurelio Menendez Jean-Pascal Pfister Alexandre Pouget Peter E. Latham
+	Variational Laplace for Bayesian neural networks	2021	Ali Ünlü Laurence Aitchison
+	A statistical theory of out-of-distribution detection.	2021	Xi Wang Laurence Aitchison
+	Undefined class-label detection vs out-of-distribution detection	2021	Xi Wang Laurence Aitchison
+	Bayesian Neural Network Priors Revisited	2021	Vincent Fortuin Adrià Garriga-Alonso Florian Wenzel Gunnar Rätsch Richard E. Turner Mark van der Wilk Laurence Aitchison
+	Tactile Image-to-Image Disentanglement of Contact Geometry from Motion-Induced Shear	2021	Anupam K. Gupta Laurence Aitchison Nathan F. Lepora
+	Bayesian OOD detection with aleatoric uncertainty and outlier exposure	2021	Xi Wang Laurence Aitchison
+	A theory of representation learning gives a deep generalisation of kernel methods	2021	Adam X. Yang Maxime Robeyns Edward Milsom Nandi Schoots Laurence Aitchison
+	A variational approximate posterior for the deep Wishart process	2021	Sebastian W. Ober Laurence Aitchison
+	InfoNCE is variational inference in a recognition parameterised model	2021	Laurence Aitchison
+	Data augmentation in Bayesian neural networks and the cold posterior effect	2021	Seth Nabarro Stoil Ganev Adrià Garriga-Alonso Vincent Fortuin Mark van der Wilk Laurence Aitchison
+	Bayesian Neural Network Priors Revisited	2021	Vincent Fortuin Adrià Garriga-Alonso Sebastian W. Ober Florian Wenzel Gunnar Rätsch Richard E. Turner Mark van der Wilk Laurence Aitchison
+	Variational Laplace for Bayesian neural networks	2021	Ali Ünlü Laurence Aitchison
+	Gradient Regularisation as Approximate Variational Inference	2020	Ali Ünlü Laurence Aitchison
+	Deep kernel processes	2020	Laurence Aitchison Adam X. Yang Sebastian W. Ober
+	A statistical theory of cold posteriors in deep neural networks	2020	Laurence Aitchison
+	A statistical theory of semi-supervised learning	2020	Laurence Aitchison
+	Global inducing point variational posteriors for Bayesian neural networks	2020	Sebastian W. Ober Laurence Aitchison
+	Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes	2020	Sebastian W. Ober Laurence Aitchison
+	Legally grounded fairness objectives	2020	Dylan Holden-Sim Gavin Leech Laurence Aitchison
+	Variational Laplace for Bayesian neural networks	2020	Ali Ünlü Laurence Aitchison
+	Semi-supervised learning objectives as log-likelihoods in a generative model of data curation	2020	Stoil Ganev Laurence Aitchison
+	A statistical theory of cold posteriors in deep neural networks	2020	Laurence Aitchison
+	Deep kernel processes	2020	Laurence Aitchison Adam X. Yang Sebastian W. Ober
+	Tensor Monte Carlo: Particle Methods for the GPU era	2019	Laurence Aitchison
+	Why bigger is not always better: on finite and infinite neural networks	2019	Laurence Aitchison
+	Why bigger is not always better: on finite and infinite neural networks	2019	Laurence Aitchison
+	Deep Convolutional Networks as shallow Gaussian Processes.	2018	Adrià Garriga-Alonso Carl Edward Rasmussen Laurence Aitchison
+	Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods	2018	Laurence Aitchison
+	Discrete flow posteriors for variational inference in discrete dynamical systems	2018	Laurence Aitchison Vincent Adam Srinivas C. Turaga
+	Tensor Monte Carlo: particle methods for the GPU era	2018	Laurence Aitchison
+	Sampling-based probabilistic inference emerges from learning in neural circuits with a cost on reliability	2018	Laurence Aitchison Guillaume Hennequin Máté Lengyel
+	Deep Convolutional Networks as shallow Gaussian Processes	2018	Adrià Garriga-Alonso Carl Edward Rasmussen Laurence Aitchison
+	Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods	2018	Laurence Aitchison
+ PDF Chat	The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics	2016	Laurence Aitchison Máté Lengyel
+	Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations	2015	Laurence Aitchison Peter E. Latham
+	Probabilistic Synapses	2014	Laurence Aitchison Alex Pouget Peter E. Latham
+	Synaptic plasticity as Bayesian inference.	2014	Laurence Aitchison Jannes Jegminat Jorge Aurelio Menendez Jean-Pascal Pfister Alex Pouget Peter E. Latham
+	The Hamiltonian Brain	2014	Laurence Aitchison Máté Lengyel
+	The Hamiltonian brain: efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics	2014	Laurence Aitchison Máté Lengyel
+	Fast sampling for Bayesian inference in neural circuits	2014	Guillaume Hennequin Laurence Aitchison Máté Lengyel
+	Zipf's law arises naturally in structured, high-dimensional data	2014	Laurence Aitchison Nicola Corradi Peter E. Latham
+	Synaptic plasticity as Bayesian inference	2014	Laurence Aitchison Jannes Jegminat Jorge Aurelio Menendez Jean-Pascal Pfister Alexandre Pouget Peter E. Latham
+	The Hamiltonian brain: efficient probabilistic inference with excitatory-inhibitory neural circuit dynamics	2014	Laurence Aitchison Máté Lengyel
+	Fast sampling for Bayesian inference in neural circuits	2014	Guillaume Hennequin Laurence Aitchison Máté Lengyel

Common Coauthors

Coauthor	Papers Together
Sebastian W. Ober	10
Adam X. Yang	7
Gavin Leech	7
Máté Lengyel	7
Adrià Garriga-Alonso	7
Peter E. Latham	6
Vincent Fortuin	6
Conor Houghton	6
Edward Milsom	5
Mark van der Wilk	5
Nathan F. Lepora	5
James Malkin	5
Cian O’Donnell	5
Xi Wang	4
Stoil Ganev	4
Ben Anson	4
Maxime Robeyns	4
Sören Mindermann	3
Jannes Jegminat	3
Jan Brauner	3
Benedict Snodin	3
Mauro Comi	3
Alessio Tonioni	3
Jorge Aurelio Menendez	3
Mrinank Sharma	3
Guillaume Hennequin	3
Yarin Gal	3
Jonas B. Sandbrink	3
Darren Smith	3
Yijiong Lin	3
Samir Bhatt	3
Jean-Pascal Pfister	3
Ali Ünlü	2
Robert Zinkov	2
Ali Ünlü	2
Richard E. Turner	2
Michele Garibbo	2
Gunnar Rätsch	2
John S. Brownstein	2
Carl Edward Rasmussen	2
Thomas Heap	2
Jack R. P. Hanslope	2
Seth Nabarro	2
Alex Church	2
Benjamin Rader	2
Alexandre Pouget	2
Florian Wenzel	2
Joshua Teperowski Monrad	2
Alex Pouget	2
Suman Ravuri	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Weight Uncertainty in Neural Networks	2015	Charles Blundell Julien Cornebise Koray Kavukcuoglu Daan Wierstra	7
+	Gaussian Process Behaviour in Wide Deep Neural Networks	2018	Alexander Matthews Mark Rowland Jiri Hron Richard E. Turner Zoubin Ghahramani	7
+	Stochastic Backpropagation and Approximate Inference in Deep Generative Models	2014	Danilo Jimenez Rezende Shakir Mohamed Daan Wierstra	7
+	Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes	2020	Sebastian W. Ober Laurence Aitchison	6
+	Bayesian Learning via Stochastic Gradient Langevin Dynamics	2011	Max Welling Yee Whye Teh	6
+	Deep Neural Networks as Gaussian Processes	2017	Jaehoon Lee Yasaman Bahri Roman Novak Samuel S. Schoenholz Jeffrey Pennington Jascha Sohl‐Dickstein	5
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	5
+	Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning	2015	Yarin Gal Zoubin Ghahramani	5
+ PDF Chat	The Hamiltonian Brain: Efficient Probabilistic Inference with Excitatory-Inhibitory Neural Circuit Dynamics	2016	Laurence Aitchison Máté Lengyel	5
+	Bayesian Neural Network Priors Revisited	2021	Vincent Fortuin Adrià Garriga-Alonso Florian Wenzel Gunnar Rätsch Richard E. Turner Mark van der Wilk Laurence Aitchison	4
+	Dendritic cortical microcircuits approximate the backpropagation algorithm	2018	João Sacramento Rui Ponte Costa Yoshua Bengio Walter Senn	4
+	A probabilistic population code based on neural samples	2018	Sabyasachi Shivkumar Richard D. Lange Ankani Chattoraj Ralf M. Haefner	4
+ PDF Chat	Singular Wishart and multivariate beta distributions	2003	Muni S. Srivastava	4
+	Variational Dropout and the Local Reparameterization Trick	2015	Diederik P. Kingma Tim Salimans Max Welling	4
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	4
+	A statistical theory of cold posteriors in deep neural networks	2020	Laurence Aitchison	4
+	On the mathematical foundations of theoretical statistics	1922	Ronald Aylmer Fisher	4
+	Spatial Point Processes	2011	Mark Huber	4
+	Synaptic plasticity as Bayesian inference	2021	Laurence Aitchison Jannes Jegminat Jorge Aurelio Menendez Jean-Pascal Pfister Alexandre Pouget Peter E. Latham	4
+ PDF Chat	Energy-efficient population coding constrains network size of a neuronal array system	2016	Lianchun Yu Chi Zhang Liwei Liu Yuguo Yu	4
+	Sparse, Flexible and Efficient Modeling using L 1 Regularization	2008	Saharon Rosset Ji Zhu	4
+	Practical Deep Learning with Bayesian Principles	2019	Kazuki Osawa Siddharth Swaroop Mohammad Emtiyaz Khan Anirudh Jain Runa Eschenhagen Richard E. Turner Rio Yokota	4
+	Two problems with variational expectation maximisation for time series models	2011	Richard E. Turner Maneesh Sahani	3
+ PDF Chat	Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification	2015	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	3
+	Deep kernel processes	2021	Laurence Aitchison Adam X. Yang Sebastian W. Ober	3
+	Noisy Natural Gradient as Variational Inference	2017	Guodong Zhang Shengyang Sun David Duvenaud Roger Grosse	3
+	Deep Convolutional Networks as shallow Gaussian Processes	2018	Adrià Garriga-Alonso Carl Edward Rasmussen Laurence Aitchison	3
+	Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning	2019	Ruqi Zhang Chunyuan Li Jianyi Zhang Changyou Chen Andrew Gordon Wilson	3
+	What Are Bayesian Neural Network Posteriors Really Like	2021	Pavel Izmailov Sharad Vikram Matthew D. Hoffman Andrew Gordon Wilson	3
+	A statistical theory of semi-supervised learning	2020	Laurence Aitchison	3
+	Exact Langevin Dynamics with Stochastic Gradients	2021	Adrià Garriga-Alonso Vincent Fortuin	3
+	Inferring the effectiveness of government interventions against COVID-19	2020	Jan Brauner Sören Mindermann Mrinank Sharma David Johnston John Salvatier Tomáš Gavenčiak Anna B. Stephenson Gavin Leech George Altman Vladimir Mikulik	3
+	The Promises and Pitfalls of Deep Kernel Learning	2021	Sebastian W. Ober Carl Edward Rasmussen Mark van der Wilk	3
+	What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?	2017	Alex Kendall Yarin Gal	3
+ PDF Chat	Learning as filtering: Implications for spike-based plasticity	2022	Jannes Jegminat Simone Carlo Surace Jean-Pascal Pfister	3
+	Wide Residual Networks	2016	Sergey Zagoruyko Nikos Komodakis	3
+	Why bigger is not always better: on finite and infinite neural networks	2019	Laurence Aitchison	3
+	Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors	2020	Michael W. Dusenberry Ghassen Jerfel Yeming Wen Yi-An Ma Jasper Snoek Katherine Heller Balaji Lakshminarayanan Dustin Tran	3
+	Importance Weighted Autoencoders	2015	Yuri Burda Roger Grosse Ruslan Salakhutdinov	3
+ PDF Chat	Identity Mappings in Deep Residual Networks	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	3
+ PDF Chat	Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe	2020	Seth Flaxman Swapnil Mishra Axel Gandy H. Juliette T. Unwin Thomas A. Mellan Helen Coupland Charles Whittaker Harrison Zhu Tresnia Berah Jeffrey W. Eaton	3
+	Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning	2021	Alexander Immer Matthias Bauer Vincent Fortuin Gunnar Rätsch Khan Emtiyaz	3
+	Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms	2017	Xiao Han Kashif Rasul Roland Vollgraf	3
+	The efficient evaluation of the hypergeometric function of a matrix argument	2006	Plamen Koev Alan Edelman	2
+	Unsupervised Data Augmentation for Consistency Training	2019	Qizhe Xie Zihang Dai Eduard Hovy Minh-Thang Luong Quoc V. Le	2
+ PDF Chat	Galaxy Zoo 2: detailed morphological classifications for 304 122 galaxies from the Sloan Digital Sky Survey	2013	Kyle Willett Chris Lintott S. P. Bamford Karen L. Masters Brooke Simmons Kevin Casteels Edward M. Edmondson L. Fortson Sugata Kaviraj William C. Keel	2
+	The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables	2016	Chris J. Maddison Andriy Mnih Yee Whye Teh	2
+	Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam	2018	Mohammad Emtiyaz Khan Didrik Nielsen Voot Tangkaratt Lin Wu Yarin Gal Akash Srivastava	2
+ PDF Chat	On Singular Wishart and Singular Multivariate Beta Distributions	1994	Harald Uhlig	2
+	WAIC, but Why? Generative Ensembles for Robust Anomaly Detection	2018	Hyunsun Choi Eric Jang Alexander A. Alemi	2