Richard Turner
33 papers · 2007–2023 · 7 conferences · across top CS/AI conferences
Achievements
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π§ Keyword Pioneer π Interdisciplinary Bridge πΊοΈ Taxonomy Completionist (20) π Renaissance Researcher (9) π£ Hot Topic Early Bird
π
Interdisciplinary Bridge
π
Academic Marathon
(16)
πΊοΈ
Taxonomy Completionist
(20)
π
Keyword Champion
(2)
π
Triple Crown
π±
Topic Pioneer
π
Century Club
(33)
ποΈ
Keyword Collector
(61)
π
Trend Setter
π₯
Unstoppable
(9)
β‘
Prolific Year
(9)
β
The Questioner
Conferences
NIPS (16)
ICML (8)
AISTATS (4)
ICLR (2)
EMNLP (1)
IJCNLP (1)
JMLR (1)
Top co-authors
Research topics
Keywords
variational inference
(9)
bayesian inference
(3)
gaussian process
(3)
expectation propagation
(3)
bayesian neural network
(3)
semi-supervised learning
(2)
neural network
(2)
task-oriented dialogue
(2)
gradient approximation
(2)
variational autoencoder
(2)
unsupervised learning
(2)
neural dialogue system
(2)
dialogue state tracking
(2)
image classification
(2)
uncertainty quantification
(2)
dialogue system
(2)
approximate inference
(2)
vision transformer
(1)
sample efficiency
(1)
stochastic gradient descent
(1)
Papers
Kronecker-Factored Approximate Curvature for Modern Neural Network Architectures
NIPS 2023
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
NIPS 2023
Geometric Neural Diffusion Processes
NIPS 2023
Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
NIPS 2022
How Tight Can PAC-Bayes be in the Small Data Regime?
NIPS 2021
Collapsed Variational Bounds for Bayesian Neural Networks
NIPS 2021
Memory Efficient Meta-Learning with Large Images
NIPS 2021
VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data
NIPS 2020
TaskNorm: Rethinking Batch Normalization for Meta-Learning
ICML 2020
On the Expressiveness of Approximate Inference in Bayesian Neural Networks
NIPS 2020
Conservative Uncertainty Estimation By Fitting Prior Networks
ICLR 2020
Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
AISTATS 2020
Scalable Exact Inference in Multi-Output Gaussian Processes
ICML 2020
Continual Deep Learning by Functional Regularisation of Memorable Past
NIPS 2020
Efficient Low Rank Gaussian Variational Inference for Neural Networks
NIPS 2020
Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes
NIPS 2020
Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling
IJCNLP 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
Meta-Learning Probabilistic Inference for Prediction
ICLR 2019
The Mirage of Action-Dependent Baselines in Reinforcement Learning
ICML 2018
Invariant Models for Causal Transfer Learning
JMLR 2018
The Geometry of Random Features
AISTATS 2018
Structured Evolution with Compact Architectures for Scalable Policy Optimization
ICML 2018
Magnetic Hamiltonian Monte Carlo
ICML 2017
On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes
AISTATS 2016
Deep Gaussian Processes for Regression using Approximate Expectation Propagation
ICML 2016
Black-Box Alpha Divergence Minimization
ICML 2016
Improving the Gaussian Process Sparse Spectrum Approximation by Representing Uncertainty in Frequency Inputs
ICML 2015
Probabilistic amplitude and frequency demodulation
NIPS 2011
Occlusive Components Analysis
NIPS 2009
Modeling Natural Sounds with Modulation Cascade Processes
NIPS 2007
On Sparsity and Overcompleteness in Image Models
NIPS 2007