Richard E. Turner

According to our database1, Richard E. Turner authored at least 75 papers between 2007 and 2020.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Other 

Links

Homepages:

On csauthors.net:

Bibliography

2020
Interpreting Spatially Infinite Generative Models.
CoRR, 2020

Diagnostic Questions: The NeurIPS 2020 Education Challenge.
CoRR, 2020

Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes.
CoRR, 2020

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data.
CoRR, 2020

Continual Deep Learning by Functional Regularisation of Memorable Past.
CoRR, 2020

TaskNorm: Rethinking Batch Normalization for Meta-Learning.
CoRR, 2020

Continual Learning with Adaptive Weights (CLAW).
Proceedings of the 8th International Conference on Learning Representations, 2020

Convolutional Conditional Neural Processes.
Proceedings of the 8th International Conference on Learning Representations, 2020

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019
STRFs in primary auditory cortex emerge from masking-based statistics of natural sounds.
PLoS Computational Biology, 2019

Differentially Private Federated Variational Inference.
CoRR, 2019

Scalable Exact Inference in Multi-Output Gaussian Processes.
CoRR, 2019

Pathologies of Factorised Gaussian and MC Dropout Posteriors in Bayesian Neural Networks.
CoRR, 2019

Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model.
CoRR, 2019

'In-Between' Uncertainty in Bayesian Neural Networks.
CoRR, 2019

Fast computation of loudness using a deep neural network.
CoRR, 2019

Improving and Understanding Variational Continual Learning.
CoRR, 2019

Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Practical Deep Learning with Bayesian Principles.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deterministic Variational Inference for Robust Bayesian Neural Networks.
Proceedings of the 7th International Conference on Learning Representations, 2019

Meta-Learning Probabilistic Inference for Prediction.
Proceedings of the 7th International Conference on Learning Representations, 2019

Semi-Supervised Bootstrapping of Dialogue State Trackers for Task-Oriented Modelling.
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019

The Gaussian Process Autoregressive Regression Model (GPAR).
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals.
Proceedings of the Symposium on Advances in Approximate Bayesian Inference, 2019

2018
Invariant Models for Causal Transfer Learning.
J. Mach. Learn. Res., 2018

Partitioned Variational Inference: A unified framework encompassing federated and continual learning.
CoRR, 2018

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks.
CoRR, 2018

Decision-Theoretic Meta-Learning: Versatile and Efficient Amortization of Few-Shot Learning.
CoRR, 2018

Infinite-Horizon Gaussian Processes.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Geometrically Coupled Monte Carlo Sampling.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Structured Evolution with Compact Architectures for Scalable Policy Optimization.
Proceedings of the 35th International Conference on Machine Learning, 2018

The Mirage of Action-Dependent Baselines in Reinforcement Learning.
Proceedings of the 6th International Conference on Learning Representations, 2018

Variational Continual Learning.
Proceedings of the 6th International Conference on Learning Representations, 2018

Gaussian Process Behaviour in Wide Deep Neural Networks.
Proceedings of the 6th International Conference on Learning Representations, 2018

Gradient Estimators for Implicit Models.
Proceedings of the 6th International Conference on Learning Representations, 2018

The Geometry of Random Features.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017
A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation.
J. Mach. Learn. Res., 2017

Approximate Inference with Amortised MCMC.
CoRR, 2017

Discriminative k-shot learning using probabilistic models.
CoRR, 2017

Interpolated Policy Gradient: Merging On-Policy and Off-Policy Gradient Estimation for Deep Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Streaming Sparse Gaussian Process Approximations.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Magnetic Hamiltonian Monte Carlo.
Proceedings of the 34th International Conference on Machine Learning, 2017

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control.
Proceedings of the 34th International Conference on Machine Learning, 2017

Tuning Recurrent Neural Networks with Reinforcement Learning.
Proceedings of the 5th International Conference on Learning Representations, 2017

Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic.
Proceedings of the 5th International Conference on Learning Representations, 2017

The Multivariate Generalised von Mises Distribution: Inference and Applications.
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016
Variational Inference with Rényi Divergence.
CoRR, 2016

A Unifying Framework for Sparse Gaussian Process Approximation using Power Expectation Propagation.
CoRR, 2016

Rényi Divergence Variational Inference.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Modelling time series via automatic learning of basis functions.
Proceedings of the 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), 2016

Black-Box Alpha Divergence Minimization.
Proceedings of the 33nd International Conference on Machine Learning, 2016

Deep Gaussian Processes for Regression using Approximate Expectation Propagation.
Proceedings of the 33nd International Conference on Machine Learning, 2016

On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
A multi-label approach to target prediction taking ligand promiscuity into account.
J. Cheminformatics, 2015

Denoising without access to clean data using a partitioned autoencoder.
CoRR, 2015

Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Stochastic Expectation Propagation.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Neural Adaptive Sequential Monte Carlo.
Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Modelling of complex signals using gaussian processes.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014
Time-Frequency Analysis as Probabilistic Inference.
IEEE Trans. Signal Process., 2014

Efficient occlusive components analysis.
J. Mach. Learn. Res., 2014

Target Fishing: A Single-Label or Multi-Label Problem?
CoRR, 2014

Tree-structured Gaussian Process Approximations.
Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

2012
Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference.
Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

2011
Probabilistic amplitude and frequency demodulation.
Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Spoken Nursery Rhymes Have a Fractal Rhythmic Structure - Evidence from Patterns of Slow Amplitude Modulation (AM).
Proceedings of the 33th Annual Meeting of the Cognitive Science Society, 2011

2010
Statistical inference for single- and multi-band Probabilistic Amplitude Demodulation.
Proceedings of the IEEE International Conference on Acoustics, 2010

2009
A Structured Model of Video Reproduces Primary Visual Cortical Organisation.
PLoS Computational Biology, 2009

Occlusive Components Analysis.
Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

2007
A Maximum-Likelihood Interpretation for Slow Feature Analysis.
Neural Computation, 2007

Modeling Natural Sounds with Modulation Cascade Processes.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

On Sparsity and Overcompleteness in Image Models.
Proceedings of the Advances in Neural Information Processing Systems 20, 2007

Probabilistic Amplitude Demodulation.
Proceedings of the Independent Component Analysis and Signal Separation, 2007


  Loading...