Raphael Suter

Representation & Self-Supervised Learning · 2018

A researcher in the Science corpus: 2 papers; trusts 29 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 31 citations into it; #2,762 of 3,145 by trust here, #2,404 by citations
• Artificial Intelligence — 28 citations into it; #30,362 of 35,345 by trust here, #21,316 by citations
• Computer Vision and Pattern Recognition — 27 citations into it
• Generative Models — 14 citations into it
• Probabilistic & Bayesian Methods — 9 citations into it; #1,155 of 1,525 by trust here, #1,176 by citations
• Causal Inference & Discovery — 7 citations into it; #312 of 364 by trust here, #224 by citations

Whom Raphael Suter trusts overall

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

1. Diederik P. Kingma
2. Jan Peters
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Max Welling
6. Jimmy Ba
7. Hyunjik Kim
8. Bernhard Schölkopf
9. Cian Eastwood
10. Karl Ridgeway

Who trusts Raphael Suter overall

1. Francesco Locatello
2. Sjoerd van Steenkiste
3. Bernhard Schölkopf
4. Stefan Bauer
5. Felix Leeb
6. Rui Shu
7. Matthew R. O'Shaughnessy
8. Olivier Bachem
9. Mengyue Yang
10. J. Urgen Schmidhuber

Papers from Raphael Suter's perspective

Ordered by how strongly the researchers Raphael Suter 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Auto-Encoding Variational Bayes (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness (2018) — 186 citations
• Interventional Robustness of Deep Latent Variable Models (2018) — 28 citations