Stephan K. Chalup

Artificial Intelligence · 2002–2008

A researcher in the Science corpus: 3 papers; trusts 56 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 35 citations into it; #14,441 of 35,345 by trust here, #25,040 by citations
• Model Architectures — 19 citations into it; #1,373 of 2,278 by trust here, #2,162 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #15,176 of 21,575 by trust here, #20,756 by citations
• Statistical Learning Theory — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Computational Theory and Mathematics — 9 citations into it
• Management Science and Operations Research — 9 citations into it; #7,768 of 11,946 by trust here, #10,893 by citations
• Accounting — 6 citations into it

Whom Stephan K. Chalup trusts overall

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

1. Jeffrey L. Elman
2. Simon S. Haykin
3. Bernhard Schölkopf
4. Lijuan Cao
5. Jordan B. Pollack
6. Vladimir Naumovich Vapni
7. Kilian Q. Weinberger
8. Barak A. Pearlmutter
9. Alex Smola
10. Christopher K. I. Williams

Who trusts Stephan K. Chalup overall

1. A. Ozbayoglu
2. Mirac Suzgun
3. Yonatan Belinkov
4. J. Urgen Schmidhuber
5. Jeffrey L. Elman
6. M. Ugur Gudelek
7. Christian Simon
8. Jiangwei Xie
9. Raghuraman Gopalan
10. Omer Berat Sezer

Papers from Stephan K. Chalup's perspective

Ordered by how strongly the researchers Stephan K. Chalup 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
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. Going deeper with convolutions

Their papers

• Incremental training of first order recurrent neural networks to predict a context-sensitive language (2003) — 32 citations
• Kernel Methods in Finance (2008) — 21 citations
• Incremental Learning in Biological and Machine Learning Systems (2002) — 33 citations