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Computer Science

D-Index
39
Citations
29043
World Ranking
9457
National Ranking
4000

Overview

Aaron van den Oord is affiliated with Google in the United States. Their research primarily spans the fields of computer science, with a particular focus on artificial intelligence, signal processing, and computer vision and pattern recognition. The scientist's work includes publications in various subfields related to audio and speech processing and machine learning applications.

The main topics addressed in their research include:

  • Music and Audio Processing
  • Speech Recognition and Synthesis
  • Domain Adaptation and Few-Shot Learning
  • Speech and Audio Processing
  • Multimodal Machine Learning Applications
  • AI in cancer detection
  • Advanced Neural Network Applications

Notable recent papers authored or co-authored by Aaron van den Oord include:

  • "Are we done with ImageNet?", 2020, published in arXiv (Cornell University)
  • "Divide and Contrast: Self-supervised Learning from Uncurated Data", 2021, presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Towards Learning Universal Audio Representations", 2022, at ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • "Efficient Visual Pretraining with Contrastive Detection", 2021, presented at the 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • "Multi-Format Contrastive Learning of Audio Representations", 2021, published in arXiv (Cornell University)

Frequent co-authors in their body of work include:

  • Luyu Wang
  • Jean-Baptiste Alayrac
  • Olivier J. Hénaff
  • Pauline Luc
  • Adrià Recasens

Aaron van den Oord's publications frequently appear in venues such as:

  • arXiv (Cornell University)
  • 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Best Publications

  • Representation Learning with Contrastive Predictive Coding

    Aaron van den Oord;Yazhe Li;Oriol Vinyals

  • WaveNet: A Generative Model for Raw Audio

    Aäron van den Oord;Sander Dieleman;Heiga Zen;Karen Simonyan

  • Neural Discrete Representation Learning

    Aaron van den Oord;Oriol Vinyals;koray kavukcuoglu

  • Conditional image generation with PixelCNN decoders

    Aäron van den Oord;Nal Kalchbrenner;Oriol Vinyals;Lasse Espeholt

  • Pixel recurrent neural networks

    Aäron Van Den Oord;Nal Kalchbrenner;Koray Kavukcuoglu

  • Deep content-based music recommendation

    Aaron van den Oord;Sander Dieleman;Benjamin Schrauwen

  • Data-Efficient Image Recognition with Contrastive Predictive Coding

    Olivier J. Hénaff;Aravind Srinivas;Jeffrey De Fauw;Ali Razavi

  • A note on the evaluation of generative models

    Lucas Theis;Aäron van den Oord;Matthias Bethge

  • Generating Diverse High-Fidelity Images with VQ-VAE-2

    Ali Razavi;Aaron van den Oord;Oriol Vinyals

  • Parallel WaveNet: Fast High-Fidelity Speech Synthesis

    Aäron van den Oord;Yazhe Li;Igor Babuschkin;Karen Simonyan

  • Neural Machine Translation in Linear Time

    Nal Kalchbrenner;Lasse Espeholt;Karen Simonyan;Aäron van den Oord

  • Efficient Neural Audio Synthesis

    Nal Kalchbrenner;Erich Elsen;Karen Simonyan;Seb Noury

  • Count-based exploration with neural density models

    Georg Ostrovski;Marc G. Bellemare;Aäron van den Oord;Rémi Munos

  • Adversarial Risk and the Dangers of Evaluating Against Weak Attacks.

    Jonathan Uesato;Brendan O'Donoghue;Aaron van den Oord;Pushmeet Kohli

  • On Variational Bounds of Mutual Information

    Ben Poole;Sherjil Ozair;Aaron van den Oord;Alexander A. Alemi

  • Unsupervised Speech Representation Learning Using WaveNet Autoencoders

    Jan Chorowski;Ron J. Weiss;Samy Bengio;Aaron van den Oord

  • Video Pixel Networks

    Nal Kalchbrenner;Aäron van den Oord;Karen Simonyan;Ivo Danihelka

  • Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video

    Lionel Pigou;Aäron van den Oord;Sander Dieleman;Mieke Van Herreweghe

  • Parallel Multiscale Autoregressive Density Estimation.

    Scott E. Reed;Aäron van den Oord;Nal Kalchbrenner;Sergio Gomez Colmenarejo

  • Are we done with ImageNet

    Lucas Beyer;Olivier J. Hénaff;Alexander Kolesnikov;Xiaohua Zhai

  • Parallel Multiscale Autoregressive Density Estimation

    Scott Reed;Aäron van den Oord;Nal Kalchbrenner;Sergio Gómez Colmenarejo

Frequent Co-Authors

Oriol Vinyals
Oriol Vinyals DeepMind (United Kingdom)
Karen Simonyan
Karen Simonyan DeepMind (United Kingdom)
Koray Kavukcuoglu
Koray Kavukcuoglu DeepMind (United Kingdom)
Alex Graves
Alex Graves Google (United States)
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Benjamin Schrauwen
Benjamin Schrauwen Ghent University
Heiga Zen
Heiga Zen Google (United States)
Danilo Jimenez Rezende
Danilo Jimenez Rezende DeepMind (United Kingdom)
Ben Poole
Ben Poole Google (United States)
Phil Blunsom
Phil Blunsom University of Oxford

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