Patrick A. Pletscher

Artificial Intelligence · 2007–2012

A researcher in the Science corpus: 3 papers; trusts 34 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 33 citations into it; #5,531 of 35,345 by trust here, #9,557 by citations
• Optimization & Training — 32 citations into it; #592 of 2,601 by trust here, #887 by citations
• Computational Mechanics — 11 citations into it; #1,385 of 10,001 by trust here, #1,838 by citations
• Computer Vision and Pattern Recognition — 10 citations into it; #9,446 of 21,575 by trust here, #14,822 by citations
• Mathematical Optimization — 7 citations into it
• Numerical Analysis — 6 citations into it
• Natural Language Processing — 5 citations into it

Whom Patrick A. Pletscher trusts overall

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

1. B. Taskar
2. Thorsten Joachims
3. Olivier Chapelle
4. Koby Crammer
5. Chun-Nam John Yu
6. Martin Jaggi
7. Yoram Singer
8. Alex Smola
9. Shai Shalev-Shwartz
10. Carlos Guestrin

Who trusts Patrick A. Pletscher overall

1. Tamir Hazan
2. Punarjay Chakravarty
3. Sebastian Nowozin
4. Alexander Gerhard Schwing
5. Hakan Bilen
6. Tinne Tuytelaars
7. Raquel Urtasun
8. Simon Lacoste-Julien
9. Jean-Baptiste Alayrac
10. Elad Hazan

Papers from Patrick A. Pletscher's perspective

Ordered by how strongly the researchers Patrick A. Pletscher 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Entropy and Margin Maximization for Structured Output Learning (2010) — 47 citations
• Block-Coordinate Frank-Wolfe Optimization for Structural SVMs (2012) — 387 citations
• Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark (2007) — 107 citations