Stephan Alaniz

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 3 papers; trusts 450 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #15,944 of 35,345 by trust here, #30,347 by citations
• Computer Vision and Pattern Recognition — 48 citations into it; #10,202 of 21,575 by trust here, #19,766 by citations
• Safety, Robustness & Interpretability — 16 citations into it
• Reinforcement Learning — 14 citations into it; #1,209 of 2,242 by trust here, #2,125 by citations
• Natural Language Processing — 10 citations into it; #4,146 of 5,688 by trust here, #5,296 by citations
• Generative Models — 9 citations into it
• Model Architectures — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it

Whom Stephan Alaniz trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Kaiming He
4. Alex Krizhevsky
5. Yann LeCun
6. Yoshua Bengio
7. Ilya Sutskever
8. Sepp Hochreiter
9. Max Welling
10. Danilo Jimenez Rezende

Who trusts Stephan Alaniz overall

1. Felix Leibfried
2. Peter Vrancx
3. Meike Nauta
4. Arthur Szlam
5. Alvin Wan
6. Ron van Bree
7. Jonathan Gray
8. Piotr Pikekos
9. Diane Bouchacourt
10. Christin Seifert

Papers from Stephan Alaniz's perspective

Ordered by how strongly the researchers Stephan Alaniz 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft (2018) — 16 citations
• Learning Decision Trees Recurrently Through Communication (2021) — 19 citations
• XOC: Explainable Observer-Classifier for Explainable Binary Decisions (2019) — 16 citations