Christian Widmer

Artificial Intelligence · 2008–2011

A researcher in the Science corpus: 6 papers; trusts 15 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it; #19,498 of 35,345 by trust here, #25,809 by citations
• Representation & Self-Supervised Learning — 18 citations into it
• Molecular Biology — 15 citations into it; #3,988 of 17,773 by trust here, #9,851 by citations
• Computer Vision and Pattern Recognition — 13 citations into it
• Statistical Learning Theory — 12 citations into it
• Computational Mechanics — 7 citations into it
• Computational Biology & Bioinformatics — 6 citations into it
• Immunology — 6 citations into it; #1,473 of 2,160 by trust here, #2,008 by citations

Whom Christian Widmer trusts overall

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

1. Theodoros Evgeniou
2. Bernhard Schölkopf
3. Chih-Jen Lin
4. Hal Daumé Iii
5. Laurent Jacob
6. Vladimir Naumovich Vapni
7. Sören Sonnenburg
8. R. Caruana
9. Alex Smola
10. Christina S. Leslie

Who trusts Christian Widmer overall

1. Yu Zhang
2. Qiang Yang
3. Wouter M. Kouw
4. Qian Xu
5. Siddharth Gopal
6. Andreas Krause
7. Pratik Jawanpuria
8. Lixin Duan
9. Marco Loog
10. Yiming Yang

Papers from Christian Widmer's perspective

Ordered by how strongly the researchers Christian Widmer 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. GENERATIVE ADVERSARIAL NETS

Their papers

• Leveraging Sequence Classification by Taxonomy-Based Multitask Learning (2010) — 85 citations
• Inferring latent task structure for Multitask Learning by Multiple Kernel Learning (2010) — 81 citations
• Novel Machine Learning Methods for MHC Class I Binding Prediction (2010) — 39 citations
• Hierarchical Multitask Structured Output Learning for Large-scale Sequence Segmentation (2011) — 49 citations
• The SHOGUN Machine Learning Toolbox (2010) — 306 citations
• An Empirical Analysis of Domain Adaptation Algorithms for Genomic Sequence Analysis (2008) — 183 citations