Michael Biehl

Molecular Biology · 1993–2016

A researcher in the Science corpus: 14 papers; trusts 15 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 31 citations into it
• Artificial Intelligence — 22 citations into it; #15,458 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 16 citations into it; #21,130 of 21,575 by trust here, #18,782 by citations
• Statistical Learning Theory — 11 citations into it
• Optimization & Training — 11 citations into it; #1,262 of 2,601 by trust here, #1,313 by citations
• Statistics and Probability — 8 citations into it
• Signal Processing — 5 citations into it

Whom Michael Biehl trusts overall

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

1. Barbara Hammer
2. Radford M. Neal
3. R. Fisher
4. James Franklin
5. John A. Hertz
6. John Shawe-Taylor
7. Thomas M. Cover
8. Thomas Villmann
9. Katherine L. Silversides
10. Anders Krogh

Who trusts Michael Biehl overall

1. Madhu S. Advani
2. Andrew M. Saxe
3. Sebastian Goldt
4. Sascha Saralajew
5. Sebastian Lee
6. Nicholas Carlini
7. M. Soltanolkotabi
8. Yu Hao
9. Lars Holdijk
10. Toshiyuki TANAKA

Papers from Michael Biehl's perspective

Ordered by how strongly the researchers Michael Biehl 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. Pattern Recognition and Machine Learning
4. Et al
5. A Stochastic Approximation Method
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. ImageNet: A large-scale hierarchical image database
10. Kernel Methods for Pattern Analysis

Their papers

• Statistical Mechanics of On-line Learning (2009) — 7 citations
• CRITICAL ASSESSMENT OF AUTOMATED FLOW CYTOMETRY DATA ANALYSIS TECHNIQUES (2013) — 629 citations
• Prototype-based models in machine learning. (2016) — 185 citations
• MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images (2015) — 365 citations
• Brain-Inspired Computing, International Workshop, Cetraro/Italy, July 2013 (2014)
• The somatic genomic landscape of chromophobe renal cell carcinoma (2014) — 802 citations
• Post‐correlation radio frequency interference classification methods (2010) — 277 citations
• Similarity-Based Clustering, Recent Developments and Biomedical Applications [outcome of a Dagstuhl Seminar] (2009) — 30 citations
• Adaptive Relevance Matrices in Learning Vector Quantization (2009) — 415 citations
• Anthropogenic disturbance changes the structure of arboreal tropical ant communities (2001) — 92 citations
• Transient dynamics of on-line learning in two-layered neural networks (1996)
• On-line backpropagation in two-layered neural networks (1995)
• Learning by on-line gradient descent (1995)
• THE STATISTICAL-MECHANICS OF LEARNING A RULE (1993)