Lukas Ruff

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 Lukas Ruff trusts overall

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

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

Who trusts Lukas Ruff overall

1. Guansong Pang
2. Jihun Yi
3. Raghavendra Chalapathy
4. Songqiao Han
5. Niv Cohen
6. Mohammadreza Salehi
7. Yiyuan Yang
8. Liron Bergman
9. Yedid Hoshen
10. Pramuditha Perera

Papers from Lukas Ruff's perspective

Ordered by how strongly the researchers Lukas Ruff 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

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