Iain Murray

Affiliations:
  • University of Edinburgh, School of Informatics, UK
  • University of Toronto, ON, Canada (former)
  • University College London, Gatsby Computational Neuroscience Unit, UK (PhD 2007)


According to our database1, Iain Murray authored at least 52 papers between 2004 and 2022.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
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PhD thesis 
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Links

Online presence:

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Bibliography

2022
Don't recommend the obvious: estimate probability ratios.
Proceedings of the RecSys '22: Sixteenth ACM Conference on Recommender Systems, Seattle, WA, USA, September 18, 2022

2021
Lossless compression with state space models using bits back coding.
CoRR, 2021

Maximum Likelihood Training of Score-Based Diffusion Models.
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Diverse Ensembles Improve Calibration.
CoRR, 2020

Density Deconvolution with Normalizing Flows.
CoRR, 2020

Ordering Dimensions with Nested Dropout Normalizing Flows.
CoRR, 2020

On Contrastive Learning for Likelihood-free Inference.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Scalable Extreme Deconvolution.
CoRR, 2019

Cubic-Spline Flows.
CoRR, 2019

Dynamic Evaluation of Transformer Language Models.
CoRR, 2019

Neural Spline Flows.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning.
Proceedings of the 36th International Conference on Machine Learning, 2019

Mode Normalization.
Proceedings of the 7th International Conference on Learning Representations, 2019

Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows.
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018
Bayesian Adversarial Spheres: Bayesian Inference and Adversarial Examples in a Noiseless Setting.
CoRR, 2018

Sequential Neural Methods for Likelihood-free Inference.
CoRR, 2018

Dynamic Evaluation of Neural Sequence Models.
Proceedings of the 35th International Conference on Machine Learning, 2018

2017
Masked Autoregressive Flow for Density Estimation.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Multiplicative LSTM for sequence modelling.
Proceedings of the 5th International Conference on Learning Representations, 2017

Aye or naw, whit dae ye hink? Scottish independence and linguistic identity on social media.
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, 2017

Markov Chain Truncation for Doubly-Intractable Inference.
Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016
Neural Autoregressive Distribution Estimation.
J. Mach. Learn. Res., 2016

Differentiation of the Cholesky decomposition.
CoRR, 2016

Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Pseudo-Marginal Slice Sampling.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
MADE: Masked Autoencoder for Distribution Estimation.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Modelling acoustic feature dependencies with artificial neural networks: Trajectory-RNADE.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014
Parallel MCMC with generalized elliptical slice sampling.
J. Mach. Learn. Res., 2014

A Deep and Tractable Density Estimator.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Attention as Reward-Driven Optimization of Sensory Processing.
Neural Comput., 2013

A framework for evaluating approximation methods for Gaussian process regression.
J. Mach. Learn. Res., 2013

Estimation Bias in Maximum Entropy Models.
Entropy, 2013

NADE: The real-valued neural autoregressive density-estimator.
CoRR, 2013

RNADE: The real-valued neural autoregressive density-estimator.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

A Composable Strategy for Shredded Document Reconstruction.
Proceedings of the Computer Analysis of Images and Patterns, 2013

2012
Deep Architectures for Articulatory Inversion.
Proceedings of the INTERSPEECH 2012, 2012

2011
The Neural Autoregressive Distribution Estimator.
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

How biased are maximum entropy models?
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

2010
Elliptical slice sampling.
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processes.
Proceedings of the UAI 2010, 2010

Slice sampling covariance hyperparameters of latent Gaussian models.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009
Evaluation methods for topic models.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities.
Proceedings of the 26th Annual International Conference on Machine Learning, 2009

2008
Characterizing response behavior in multisensory perception with conflicting cues.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Evaluating probabilities under high-dimensional latent variable models.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

The Gaussian Process Density Sampler.
Proceedings of the Advances in Neural Information Processing Systems 21, 2008

On the quantitative analysis of deep belief networks.
Proceedings of the Machine Learning, 2008

2006
MCMC for Doubly-intractable Distributions.
Proceedings of the UAI '06, 2006

2005
Nested sampling for Potts models.
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

A Pragmatic Bayesian Approach to Predictive Uncertainty.
Proceedings of the Machine Learning Challenges, 2005

2004
Bayesian Learning in Undirected Graphical Models: Approximate MCMC Algorithms.
Proceedings of the UAI '04, 2004


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