Aäron van den Oord

28.3k citations
25 papers · 1.2k indexed · 1 hit paper · h-index 16

Aäron van den Oord

25 papers receiving 1.1k citations

Hit Papers

Deep content-based music recommendation6292013202620172021200400600

Peers

Aäron van den Oord
Comparison fields: 5 of 102
  • Signal Processing 419
  • Computer Vision and Pattern Recognition 492
  • Artificial Intelligence 637
  • Information Systems 386
  • Computational Mathematics 8
Replace Xiangyang Luo with:
Xiangyang Luo China
Lixin Han China
Angela Fan United States
H. Vicky Zhao China
Pedro J. Moreno United States
Stephen M. Chu United States
Jizhong Han China
Yansong Feng China
Mehdi Hosseinzadeh Aghdam Iran
Yingxia Shao China
Aäron van den Oord relative to Xiangyang Luo China Xiangyang Luo's profile →
Citations per field
00.5×11.8×
Xiangyang Luo · 1×
Citations per year

Countries citing papers authored by Aäron van den Oord

Since Specialization
Citations

This map shows the geographic impact of Aäron van den Oord's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Aäron van den Oord with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Aäron van den Oord more than expected).

Fields of papers citing papers by Aäron van den Oord

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aäron van den Oord. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Aäron van den Oord. The network helps show where Aäron van den Oord may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Aäron van den Oord, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Aäron van den Oord Line = papers co-authored together Aäron van den Oord links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202120
2
Self-supervised Adversarial Robustness for the Low-label, High-data Regime
20216
3 202044
4 20206
5
Shaping Belief States with Generative Environment Models for RL
20197
6
Generating Diverse High-Resolution Images with VQ-VAE
20197
7 201976
8
Unsupervised Learning of Efficient and Robust Speech Representations
20193
9
Associative Compression Networks
20182
10
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks.
201831
11
Sample-efficient adaptive text-to-speech
201816
12
The challenge of realistic music generation: modelling raw audio at scale
201820
13
Visual Imitation with a Minimal Adversary
20181
14
Count-based exploration with neural density models
201743
15
Locally-connected transformations for deep GMMs
20154
16
Factoring Variations in Natural Images with Deep Gaussian Mixture Models
201432
17 201417
18
Deep content-based music recommendationbreakdown →
2013629
19 20131
20 201217

About Aäron van den Oord

Aäron van den Oord is a scholar working on Signal Processing, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 25 papers that have together received 1.2k indexed citations. Recurring topics across this work include Speech and Audio Processing (10 papers), Music and Audio Processing (9 papers), Speech Recognition and Synthesis (6 papers), Generative Adversarial Networks and Image Synthesis (4 papers), Advanced Data Compression Techniques (4 papers), AI in cancer detection (3 papers), Music Technology and Sound Studies (3 papers) and Adversarial Robustness in Machine Learning (3 papers). The work is most often cited by research in Signal Processing (419 citations), Computer Vision and Pattern Recognition (492 citations) and Artificial Intelligence (637 citations). Aäron van den Oord has collaborated with scholars based in United States, Belgium and United Kingdom. Frequent co-authors include Benjamin Schrauwen, Sander Dieleman, Oriol Vinyals, Luyu Wang, Thomas C. Walters, Felicia S. C. Lim, Yazhe Li, Georg Ostrovski, Marc G. Bellemare and Kazuya Kawakami. Their work appears in journals such as Communications of the ACM, Journal of Machine Learning Research and 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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