Benjamin van Niekerk

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 3 papers; trusts 48 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #14,804 of 35,345 by trust here, #15,866 by citations
• Speech & Audio — 65 citations into it; #730 of 1,076 by trust here, #694 by citations
• Signal Processing — 54 citations into it; #4,846 of 7,572 by trust here, #3,099 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Natural Language Processing — 17 citations into it
• Computer Vision and Pattern Recognition — 15 citations into it
• Evaluation & Benchmarks — 7 citations into it
• Cognitive Science & Psychology — 6 citations into it

Whom Benjamin van Niekerk trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Aäron van den Oord
2. Diederik P. Kingma
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Oriol Vinyals
6. Okko Johannes Räsänen
7. Jimmy Ba
8. A. van den Oord
9. Alex Graves
10. Thomas Schatz

Who trusts Benjamin van Niekerk overall

1. Shijun Wang
2. Lasse Borgholt
3. Abdel-rahman Mohamed
4. Evgeny Kharitonov
5. Adam Polyak
6. Ewan Dunbar
7. Jakob Drachmann Havtorn
8. Damian Borth
9. Jade Copet
10. Alexei Baevski

Papers from Benjamin van Niekerk's perspective

Ordered by how strongly the researchers Benjamin van Niekerk trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Vector-quantized neural networks for acoustic unit discovery in the ZeroSpeech 2020 challenge (2020) — 127 citations
• Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks (2019) — 59 citations
• Analyzing Speaker Information in Self-Supervised Models to Improve Zero-Resource Speech Processing (2021) — 37 citations