Richard Turner
32 papers · 2007–2023 · 6 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (73) π Academic Marathon Β· Full π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Gold π£ Hot Topic Early Bird
π±
Topic Pioneer
π¬
Deep Specialist
(10)
π
Triple Crown
π
Keyword Trendsetter Combo
(3)
π
Keyword Champion
(2)
π§
Keyword Pioneer
π₯
Unstoppable Β· Bronze
π
Trend Setter
β
The Questioner
ποΈ
Keyword Collector
(178)
π
Renaissance Researcher
(9)
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Century Club Β· Bronze
β‘
Prolific Year Β· Productive
Top co-authors
Research topics
Machine Learning
(26)
Optimization & Theory
(16)
Artificial Intelligence
(15)
Deep Learning
(15)
Bayesian & Probabilistic Β· Artificial Intelligence
(11)
Core Methods
(10)
Bayesian & Probabilistic Β· Machine Learning
(8)
Learning Types Β· Machine Learning
(8)
Models
(8)
Learning Types Β· Deep Learning
(6)
Computer Vision
(4)
Analysis
(4)
Learning Paradigms
(4)
Mathematics & Optimization
(3)
Application Areas
(3)
Keywords
variational inference
(10)
bayesian neural network
(3)
gaussian process
(3)
expectation propagation
(3)
bayesian inference
(3)
image classification
(2)
unsupervised learning
(2)
probabilistic inference
(2)
gradient approximation
(2)
variational autoencoder
(2)
uncertainty estimation
(2)
approximate inference
(2)
uncertainty quantification
(2)
few-shot learning
(2)
vision transformer
(1)
semi-supervised learning
(1)
sample efficiency
(1)
stochastic gradient descent
(1)
neural network compression
(1)
catastrophic forgetting
(1)
Papers
Geometric Neural Diffusion Processes
NeurIPS 2023
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
NeurIPS 2023
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
NeurIPS 2023
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
NeurIPS 2022
Collapsed Variational Bounds for Bayesian Neural Networks
NeurIPS 2021
How Tight Can PAC-Bayes be in the Small Data Regime?
NeurIPS 2021
Memory Efficient Meta-Learning with Large Images
NeurIPS 2021
Conservative Uncertainty Estimation By Fitting Prior Networks
ICLR 2020
Continual Deep Learning by Functional Regularisation of Memorable Past
NeurIPS 2020
Efficient Low Rank Gaussian Variational Inference for Neural Networks
NeurIPS 2020
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
AISTATS 2020
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
NeurIPS 2020
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
NeurIPS 2020
Scalable Exact Inference in Multi-Output Gaussian Processes
ICML 2020
TaskNorm: Rethinking Batch Normalization for Meta-Learning
ICML 2020
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
NeurIPS 2020
Meta-Learning Probabilistic Inference for Prediction
ICLR 2019
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
AISTATS 2019
Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling
EMNLP 2019
Invariant Models for Causal Transfer Learning
JMLR 2018
Structured Evolution with Compact Architectures for Scalable Policy Optimization
ICML 2018
The Geometry of Random Features
AISTATS 2018
The Mirage of Action-Dependent Baselines in Reinforcement Learning
ICML 2018
Magnetic Hamiltonian Monte Carlo
ICML 2017
Black-Box Alpha Divergence Minimization
ICML 2016
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
ICML 2016
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes
AISTATS 2016
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
ICML 2015
Probabilistic amplitude and frequency demodulation
NeurIPS 2011
Occlusive Components Analysis
NeurIPS 2009
Modeling Natural Sounds with Modulation Cascade Processes
NeurIPS 2007
On Sparsity and Overcompleteness in Image Models
NeurIPS 2007