Robert A. Vandermeulen

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 103 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 286 citations into it
• Computer Vision and Pattern Recognition — 122 citations into it
• Representation & Self-Supervised Learning — 67 citations into it
• Computer Networks and Communications — 63 citations into it
• Signal Processing — 50 citations into it
• Generative Models — 38 citations into it
• Probabilistic & Bayesian Methods — 36 citations into it
• Statistics and Probability — 36 citations into it

Whom Robert A. Vandermeulen trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Yann LeCun
6. Kaiming He
7. Ilya Sutskever
8. Radford M. Neal
9. Max Welling
10. Ruslan Salakhutdinov

Who trusts Robert A. Vandermeulen overall

1. Jihun Yi
2. Guansong Pang
3. Songqiao Han
4. Lukas Ruff
5. Mohammadreza Salehi

Papers from Robert A. Vandermeulen's perspective

Ordered by how strongly the researchers Robert A. Vandermeulen trusts endorse each paper. Same corpus, a different order for every researcher.

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

Their papers

• A Unifying Review of Deep and Shallow Anomaly Detection (2020) — 1,067 citations
• Deep Semi-Supervised Anomaly Detection (2019) — 704 citations
• Deep One-Class Classification (2018) — 2,692 citations