World's Best Scientists 2026 revealed!

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Computer Science

D-Index
39
Citations
7096
World Ranking
9693
National Ranking
159

Overview

Ron Meir is affiliated with the Technion - Israel Institute of Technology in Israel. Their research primarily spans across the field of Computer Science with a focus on subfields including Artificial Intelligence, Molecular Biology, Cognitive Neuroscience, Electrical and Electronic Engineering, and Management Science and Operations Research.

The scientist's recent publications cover a variety of topics and venues. Notable works include:

  • Cell-Type-Specific Outcome Representation in the Primary Motor Cortex, 2020, Neuron
  • Discount Factor as a Regularizer in Reinforcement Learning, 2020, arXiv (Cornell University)
  • Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN, 2024, arXiv (Cornell University)
  • A Theory of the Distortion-Perception Tradeoff in Wasserstein Space, 2021, arXiv (Cornell University)
  • Identifying regulation with adversarial surrogates, 2023, Proceedings of the National Academy of Sciences

The frequent co-authors collaborating with Ron Meir are:

  • Ron Teichner
  • Boaz Carmeli
  • Yonatan Belinkov
  • Naama Brenner
  • Hadas Benisty

Publication venues where the scientist regularly contributes include:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Neuron
  • Proceedings of the National Academy of Sciences

The main research topics of Ron Meir consist of:

  • Gene Regulatory Network Analysis
  • Neural dynamics and brain function
  • Advanced Bandit Algorithms Research
  • Evolutionary Algorithms and Applications
  • Reinforcement Learning in Robotics
  • Fault Detection and Control Systems
  • EEG and Brain-Computer Interfaces

Best Publications

  • The kernel recursive least-squares algorithm

    Y. Engel;S. Mannor;R. Meir

  • An introduction to boosting and leveraging

    Ron Meir;Gunnar Rätsch

  • Reinforcement learning with Gaussian processes

    Yaakov Engel;Shie Mannor;Ron Meir

  • Bayes meets bellman: the Gaussian process approach to temporal difference learning

    Yaakov Engel;Shie Mannor;Ron Meir

  • Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights

    Daniel Soudry;Itay Hubara;Ron Meir

  • Sparse Online Greedy Support Vector Regression

    Yaakov Engel;Shie Mannor;Ron Meir

  • Almost Linear VC Dimension Bounds for Piecewise Polynomial Networks

    Peter L. Bartlett;Vitaly Maiorov;Ron Meir

  • Extracting grid cell characteristics from place cell inputs using non-negative principal component analysis

    Yedidyah Dordek;Daniel Soudry;Ron Meir;Dori Derdikman

  • Generalization error bounds for Bayesian mixture algorithms

    Ron Meir;Tong Zhang

  • A Parallel Gradient Descent Method for Learning in Analog VLSI Neural Networks

    J. Alspector;R. Meir;B. Yuhas;A. Jayakumar

  • Computing with arrays of coupled oscillators: an application to preattentive texture discrimination

    Pierre Baldi;Ronny Meir

  • Nonparametric Time Series Prediction Through Adaptive ModelSelection

    Ron Meir

  • Semantic-oriented 3d shape retrieval using relevance feedback

    George Leifman;Ron Meir;Ayellet Tal

  • Layered neural networks

    E Domany;W Kinzel;R Meir

  • Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory

    Ron Amit;Ron Meir

  • Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule

    Dorit Baras;Ron Meir

  • Learning by choice of internal representations

    Tal Grossman;Ronny Meir;Eytan Domany

  • Density estimation through convex combinations of densities: approximation and estimation bounds

    Assaf J. Zeevi;Ronny Meir

  • Approximation bounds for smooth functions in C(R/sup d/) by neural and mixture networks

    V. Maiorov;R.S. Meir

  • Towards Behaviometric Security Systems: Learning to Identify a Typist

    Mordechai Nisenson;Ido Yariv;Ran El-Yaniv;Ron Meir

Frequent Co-Authors

Daniel Soudry
Daniel Soudry Technion – Israel Institute of Technology
Manfred Opper
Manfred Opper Technical University of Berlin
Shie Mannor
Shie Mannor Technion – Israel Institute of Technology
Ran El-Yaniv
Ran El-Yaniv Technion – Israel Institute of Technology
Tong Zhang
Tong Zhang University of Illinois at Urbana-Champaign
Aviv Tamar
Aviv Tamar Technion – Israel Institute of Technology
Assaf Zeevi
Assaf Zeevi Columbia University
Peter Auer
Peter Auer University of Leoben
Brett D. Mensh
Brett D. Mensh Howard Hughes Medical Institute
Yonina C. Eldar
Yonina C. Eldar Weizmann Institute of Science

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