← Authors

Richard Turner

33 papers · 2007–2023 · 7 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌍 Conference Polyglot (7) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (8) 🌈 Renaissance Researcher (9) πŸŒ‰ Interdisciplinary Bridge πŸƒ Academic Marathon (16) πŸ—ΊοΈ Taxonomy Completionist (20) πŸ† Keyword Champion (2) πŸ‘‘ Triple Crown 🌱 Topic Pioneer πŸ’Ž Century Club (33) πŸ—ƒοΈ Keyword Collector (61) πŸ“ˆ Trend Setter πŸ”₯ Unstoppable (9) ⚑ Prolific Year (9) ❓ The Questioner

Conferences

NIPS (16) ICML (8) AISTATS (4) ICLR (2) EMNLP (1) IJCNLP (1) JMLR (1)

Top co-authors

Sebastian Nowozin (5) Andrew Foong (4) John Bronskill (4) Mark Rowland (4) Maneesh Sahani (4) Wessel Bruinsma (3) Jonathan Gordon (3) Katja Hofmann (3) Yingzhen Li (3) Siddharth Swaroop (3)

Research topics

Processing (1)

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