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Danilo Jimenez Rezende

Danilo Jimenez Rezende

D-Index & Metrics

Computer Science

D-Index
47
Citations
21864
World Ranking
6287
National Ranking
377

Overview

Danilo Jimenez Rezende is affiliated with DeepMind in the United Kingdom. Their research spans the fields of Computer Science and Physics and Astronomy, with a focus on Artificial Intelligence, Nuclear and High Energy Physics, Computer Vision and Pattern Recognition, Molecular Biology, and Condensed Matter Physics.

The main topics of their work include Quantum Chromodynamics and Particle Interactions, Particle physics theoretical and experimental studies, Generative Adversarial Networks and Image Synthesis, Theoretical and Computational Physics, Computational Physics and Python Applications, Domain Adaptation and Few-Shot Learning, and Neural Networks and Applications.

Danilo Jimenez Rezende has published extensively across several venues. The most frequent publication platforms include:

  • arXiv (Cornell University)
  • Physical review. D/Physical review. D.
  • Physical Review Letters
  • Frontiers in Computational Neuroscience
  • The European Physical Journal A

Some of their recent papers are:

  • "Equivariant Flow-Based Sampling for Lattice Gauge Theory", 2020, Physical Review Letters
  • "Sampling using SU(N) gauge equivariant flows", 2021, Physical review. D/Physical review. D.
  • "Normalizing Flows on Tori and Spheres", 2020, arXiv (Cornell University)
  • "Gauge-equivariant flow models for sampling in lattice field theories with pseudofermions", 2022, Physical review. D/Physical review. D.
  • "Flow-based sampling in the lattice Schwinger model at criticality", 2022, Physical review. D/Physical review. D.

Frequent collaborators in their work include Sébastien Racanière, Phiala E. Shanahan, Gurtej Kanwar, Denis Boyda, and K. Cranmer. These co-authors have been involved in multiple publications, reflecting sustained research partnerships.

Best Publications

  • Stochastic Backpropagation and Approximate Inference in Deep Generative Models

    Danilo Jimenez Rezende;Shakir Mohamed;Daan Wierstra

  • Semi-supervised Learning with Deep Generative Models

    Diederik P Kingma;Shakir Mohamed;Danilo Jimenez Rezende;Max Welling

  • Variational Inference with Normalizing Flows

    Danilo Rezende;Shakir Mohamed

  • Semi-Supervised Learning with Deep Generative Models

    Diederik P. Kingma;Danilo J. Rezende;Shakir Mohamed;Max Welling

  • DRAW: A Recurrent Neural Network For Image Generation

    Karol Gregor;Ivo Danihelka;Alex Graves;Danilo Rezende

  • Interaction networks for learning about objects, relations and physics

    Peter Battaglia;Razvan Pascanu;Matthew Lai;Danilo Jimenez Rezende

  • Normalizing flows for probabilistic modeling and inference

    George Papamakarios;Eric T. Nalisnick;Danilo Jimenez Rezende;Shakir Mohamed

  • Neural scene representation and rendering

    S. M. Ali Eslami;Danilo Jimenez Rezende;Frederic Besse;Fabio Viola

  • Variational information maximisation for intrinsically motivated reinforcement learning

    Shakir Mohamed;Danilo J. Rezende

  • Towards a Definition of Disentangled Representations

    Irina Higgins;David Amos;David Pfau;Sébastien Racanière

  • Unsupervised Learning of 3D Structure from Images

    Danilo Jimenez Rezende;S. M. Ali Eslami;Shakir Mohamed;Peter W. Battaglia

  • A Probabilistic U-Net for Segmentation of Ambiguous Images

    Simon A. A. Kohl;Bernardino Romera-Paredes;Clemens Meyer;Jeffrey De Fauw

  • Conditional Neural Processes

    Marta Garnelo;Dan Rosenbaum;Christopher Maddison;Tiago Ramalho

  • Imagination-Augmented Agents for Deep Reinforcement Learning

    Sébastien Racanière;Theophane Weber;David P. Reichert;Lars Buesing

  • One-shot generalization in deep generative models

    Danilo J. Rezende;Shakir Mohamed;Ivo Danihelka;Karol Gregor

  • Variational inference for Monte Carlo objectives

    Andriy Mnih;Danilo J. Rezende

  • Imagination-Augmented Agents for Deep Reinforcement Learning

    Théophane Weber;Sébastien Racanière;David P. Reichert;Lars Buesing

  • Variational Intrinsic Control

    Karol Gregor;Danilo Jimenez Rezende;Daan Wierstra

  • Towards Conceptual Compression

    Karol Gregor;Frederic Besse;Danilo Jimenez Rezende;Ivo Danihelka

  • Unsupervised Predictive Memory in a Goal-Directed Agent

    Greg Wayne;Chia-Chun Hung;David Amos;Mehdi Mirza

  • Neural Processes

    Marta Garnelo;Jonathan Schwarz;Dan Rosenbaum;Fabio Viola

Frequent Co-Authors

Daan Wierstra
Daan Wierstra DeepMind (United Kingdom)
Peter W. Battaglia
Peter W. Battaglia DeepMind (United Kingdom)
Demis Hassabis
Demis Hassabis Google (United States)
Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Timothy P. Lillicrap
Timothy P. Lillicrap University College London
Murray Shanahan
Murray Shanahan Imperial College London
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Razvan Pascanu
Razvan Pascanu DeepMind (United Kingdom)
Wulfram Gerstner
Wulfram Gerstner École Polytechnique Fédérale de Lausanne
Matthew Botvinick
Matthew Botvinick Yale University

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