Thomas Villmann

Artificial Intelligence · 2002–2019

A researcher in the Science corpus: 8 papers; trusts 125 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 64 citations into it; #18,412 of 35,345 by trust here, #25,809 by citations
• Computer Vision and Pattern Recognition — 50 citations into it; #8,309 of 21,575 by trust here, #16,367 by citations
• Statistics and Probability — 17 citations into it
• Representation & Self-Supervised Learning — 16 citations into it
• Optimization & Training — 15 citations into it
• Safety, Robustness & Interpretability — 14 citations into it
• Model Architectures — 13 citations into it
• Statistical Learning Theory — 10 citations into it

Whom Thomas Villmann trusts overall

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

1. Kari Torkkola
2. Geoffrey E. Hinton
3. Barbara Hammer
4. L. V. D. van der Maaten
5. Radford M. Neal
6. Diederik P. Kingma
7. Shun-ichi Amari
8. Alex Krizhevsky
9. Yann LeCun
10. Yoshua Bengio

Who trusts Thomas Villmann overall

1. Barnab'as P'oczos
2. Michael Biehl
3. Andreas Backhaus
4. Zoltán Szabó
5. Petra Schneider
6. Jeff Schneider
7. Sascha Saralajew
8. Sven Haase
9. Udo Seiffert

Papers from Thomas Villmann's perspective

Ordered by how strongly the researchers Thomas Villmann 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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

• Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components (2019) — 28 citations
• Prototype-based Neural Network Layers: Incorporating Vector Quantization (2018) — 15 citations
• Types of (dis-)similarities and adaptive mixtures thereof for improved classification learning (2017) — 34 citations
• Prototype-based models in machine learning. (2016) — 185 citations
• Brain-Inspired Computing, International Workshop, Cetraro/Italy, July 2013 (2014)
• Similarity-Based Clustering, Recent Developments and Biomedical Applications [outcome of a Dagstuhl Seminar] (2009) — 30 citations
• Mathematical Aspects of Divergence Based Vector Quantization Using Fr´ echet-Derivatives (2009) — 29 citations
• Generalized relevance learning vector quantization (2002) — 473 citations