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Radford M. Neal

Radford M. Neal

D-Index & Metrics

Engineering and Technology

D-Index
46
Citations
49421
World Ranking
5017
National Ranking
203

Overview

Radford M. Neal is affiliated with the University of Toronto in Canada. Their research spans several fields primarily within computer science and mathematics. They have contributed notably to the subfields of statistics and probability, artificial intelligence, condensed matter physics, ocean engineering, and computer vision and pattern recognition.

The following main topics characterize Neal's research work:

  • Markov Chains and Monte Carlo Methods
  • Gaussian Processes and Bayesian Inference
  • Machine Learning and Algorithms
  • Theoretical and Computational Physics
  • Statistical Methods and Bayesian Inference
  • Automated Road and Building Extraction
  • Video Surveillance and Tracking Methods

Neal has published frequently in venues such as:

  • arXiv (Cornell University)
  • Journal of Applied Probability

Some of their recent papers include:

  • Non-reversibly updating a uniform [0,1] value for Metropolis accept/reject decisions, 2020, arXiv (Cornell University)
  • Efficiency of reversible MCMC methods: elementary derivations and applications to composite methods, 2023, arXiv (Cornell University)
  • Modifying Gibbs sampling to avoid self transitions, 2024, arXiv (Cornell University)

Neal has collaborated with several researchers frequently, including:

  • Jeffrey S. Rosenthal
  • Adu-Gyamfi Kojo
  • Kandiboina Raghupathi
  • R. Varsha
  • Knickerbocker Skylar

Their work integrates theoretical and computational approaches, contributing to areas such as Markov chain Monte Carlo methodologies and Bayesian inference. Additionally, Neal has participated in research related to automated extraction of infrastructure using deep learning techniques, reflecting interdisciplinary applications within artificial intelligence and computer vision.

Best Publications

  • Bayesian learning for neural networks

    Geoffrey Hinton;Radford M. Neal

  • Near Shannon limit performance of low density parity check codes

    David J. C. MacKay;Radford M. Neal

  • Arithmetic coding for data compression

    Ian H. Witten;Radford M. Neal;John G. Cleary

  • A view of the EM algorithm that justifies incremental, sparse, and other variants

    Radford M. Neal;Geoffrey E. Hinton

  • Markov Chain Sampling Methods for Dirichlet Process Mixture Models

    Radford M. Neal

  • MCMC Using Hamiltonian Dynamics

    Radford M. Neal

  • Slice Sampling

    Radford M. Neal

  • The helmholtz machine

    Peter Dayan;Geoffrey E. Hinton;Radford M. Neal;Richard S. Zemel

  • Annealed importance sampling

    Radford M. Neal

  • The "Wake-Sleep" Algorithm for Unsupervised Neural Networks

    Geoffrey E. Hinton;Peter Dayan;Brendan J. Frey;Radford M. Neal

  • Markov Chain Monte Carlo in Practice: A Roundtable Discussion

    Robert E. Kass;Bradley P. Carlin;Andrew Gelman;Radford M. Neal

  • Arithmetic coding revisited

    Alistair Moffat;Radford M. Neal;Ian H. Witten

  • Connectionist learning of belief networks

    Radford M. Neal

  • A Split-Merge Markov chain Monte Carlo Procedure for the Dirichlet Process Mixture Model

    Sonia Jain;Radford M Neal

  • Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification

    Radford M. Neal

  • Sampling from multimodal distributions using tempered transitions

    Radford M. Neal

  • Regression and Classification Using Gaussian Process Priors

    Radford M. Neal

  • Priors for Infinite Networks

    Radford M. Neal

  • Bayesian Mixture Modeling

    Radford M. Neal

  • Nonlinear Models Using Dirichlet Process Mixtures

    Babak Shahbaba;Radford Neal

  • Bayesian Learning via Stochastic Dynamics

    Radford M. Neal

  • Bayesian Methods for Adaptive Models

    John Bridle;Peter Cheeseman;Sidney Fels;Steve Gull

Frequent Co-Authors

Geoffrey E. Hinton
Geoffrey E. Hinton University of Toronto
Peter Dayan
Peter Dayan Max Planck Institute for Biological Cybernetics
Sam T. Roweis
Sam T. Roweis New York University
Ian H. Witten
Ian H. Witten University of Waikato
Alistair Moffat
Alistair Moffat University of Melbourne
Richard S. Zemel
Richard S. Zemel University of Toronto
Brian Wyvill
Brian Wyvill University of Victoria
Jennifer Listgarten
Jennifer Listgarten University of California, Berkeley
Zoubin Ghahramani
Zoubin Ghahramani University of Cambridge
Andrew Emili
Andrew Emili Boston University

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