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Computer Science

D-Index
38
Citations
13933
World Ranking
9956
National Ranking
622

Overview

Iain Murray is a researcher affiliated with the University of Edinburgh in the United Kingdom, specializing primarily in Computer Science. Their work notably intersects with the fields of Artificial Intelligence, Computer Vision and Pattern Recognition, Environmental Engineering, Surgery, and Statistical and Nonlinear Physics.

Their research contributions span several key topics, including:

  • Generative Adversarial Networks and Image Synthesis
  • Gaussian Processes and Bayesian Inference
  • Model Reduction and Neural Networks
  • Anomaly Detection Techniques and Applications
  • Air Quality Monitoring and Forecasting
  • Traffic Prediction and Management Techniques
  • Urban Heat Island Mitigation

Murray's publication record includes papers in varied and recognized venues. The most frequent publication venues are:

  • arXiv (Cornell University)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • European Journal of Orthopaedic Surgery & Traumatology
  • Public Money & Management
  • Zenodo (CERN European Organization for Nuclear Research)

Recent papers authored or coauthored by Murray illustrate a focus on machine learning techniques and models applied to complex data streams and inference problems. These papers include:

  • "nflows: normalizing flows in PyTorch" (2020), published in Zenodo (CERN European Organization for Nuclear Research)
  • "Maximum Likelihood Training of Score-Based Diffusion Models" (2021), published on arXiv (Cornell University)
  • "CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting" (2021), published in the Proceedings of the AAAI Conference on Artificial Intelligence
  • "On Contrastive Learning for Likelihood-free Inference" (2020), published on arXiv (Cornell University)
  • "Diverse Ensembles Improve Calibration" (2020), published on arXiv (Cornell University)

The scientist collaborates frequently with other researchers, including:

  • Conor Durkan
  • Artur Bekasov
  • George Papamakarios
  • Chaoyun Zhang
  • Marco Fiore

Best Publications

  • Proceedings of The 32nd International Conference on Machine Learning

    Mathieu Germain;Karol Gregor;Iain Murray;Hugo Larochelle

  • Evaluation methods for topic models

    Hanna M. Wallach;Iain Murray;Ruslan Salakhutdinov;David Mimno

  • Masked Autoregressive Flow for Density Estimation

    George Papamakarios;Theo Pavlakou;Iain Murray

  • MADE: Masked Autoencoder for Distribution Estimation

    Mathieu Germain;Karol Gregor;Iain Murray;Hugo Larochelle

  • The Neural Autoregressive Distribution Estimator

    Hugo Larochelle;Iain Murray

  • On the quantitative analysis of deep belief networks

    Ruslan Salakhutdinov;Iain Murray

  • MCMC for doubly-intractable distributions

    Iain Murray;Zoubin Ghahramani;David J. C. MacKay

  • Elliptical slice sampling

    Iain Murray;Ryan Prescott Adams;David J.C. MacKay

  • Advances in Neural Information Processing Systems 18

    Iain Murray;David J. C. MacKay;Zoubin Ghahramani;John Skilling

  • Neural autoregressive distribution estimation

    Benigno Uria;Marc-Alexandre Côté;Karol Gregor;Iain Murray

  • Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities

    Ryan Prescott Adams;Iain Murray;David J. C. MacKay

  • Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010)

    Ryan Prescott Adams;George E. Dahl;Iain Murray

  • Slice sampling covariance hyperparameters of latent Gaussian models

    Iain Murray;Ryan P Adams

  • Fast $ε$-free Inference of Simulation Models with Bayesian Conditional Density Estimation

    George Papamakarios;Iain Murray

  • Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

    George Papamakarios;David C. Sterratt;Iain Murray

  • A framework for evaluating approximation methods for Gaussian process regression

    Krzysztof Chalupka;Christopher K. I. Williams;Iain Murray

  • Neural Spline Flows

    Conor Durkan;Artur Bekasov;Iain Murray;George Papamakarios

  • RNADE: The real-valued neural autoregressive density-estimator

    Benigno Uria;Iain Murray;Hugo Larochelle

  • A Deep and Tractable Density Estimator

    Benigno Uria;Iain Murray;Hugo Larochelle

  • 30th Conference on Neural Information Processing Systems (NIPS 2016)

    George Papamakarios;Iain Murray

  • Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS)

    Iain Murray;Ryan Prescott Adams;David J. C. MacKay

  • Proceedings of the 14th International Conference on Artificial Intelligence and Statistics (AISTATS)

    Hugo Larochelle;Iain Murray

  • Fast $psilon$-free Inference of Simulation Models with Bayesian Conditional Density Estimation

    George Papamakarios;Iain Murray

Frequent Co-Authors

Ryan P. Adams
Ryan P. Adams Princeton University
David J. C. MacKay
David J. C. MacKay University of Cambridge
Hugo Larochelle
Hugo Larochelle Google (United States)
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Steve Renals
Steve Renals University of Edinburgh
David W. Hogg
David W. Hogg Max Planck Society
George E. Dahl
George E. Dahl Google (United States)
Mark Girolami
Mark Girolami University of Cambridge
Ruslan Salakhutdinov
Ruslan Salakhutdinov Carnegie Mellon University
Jakob H. Macke
Jakob H. Macke Max Planck Institute for Intelligent Systems

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