Paul Sajda

Artificial Intelligence · 2005–2009

A researcher in the Science corpus: 3 papers; trusts 39 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 15 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Cognitive Neuroscience — 9 citations into it; #9,941 of 10,852 by trust here, #5,528 by citations
• Molecular Biology — 7 citations into it; #2,150 of 17,773 by trust here, #13,563 by citations
• Signal Processing — 7 citations into it
• Statistical Learning Theory — 6 citations into it
• Probabilistic & Bayesian Methods — 6 citations into it
• Representation & Self-Supervised Learning — 5 citations into it

Whom Paul Sajda trusts overall

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

1. Bernhard Schölkopf
2. Vladimir Naumovich Vapni
3. Patrik O. Hoyer
4. Lawrence R. Rabiner
5. Arthur P. Dempster
6. Herbert Ramoser
7. Alex Smola
8. Zoubin Ghahramani
9. Anthony J. Bell
10. Edward H. Shortliffe

Who trusts Paul Sajda overall

1. G. Handelman
2. Guoguang Rong
3. Fan Zhang
4. H. Kok
5. Arnaldo Mendez
6. Shijun Wang
7. Warren He
8. Raymond Cheng
9. R. Chandra
10. Elie Bou Assi

Papers from Paul Sajda's perspective

Ordered by how strongly the researchers Paul Sajda 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. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Machine learning for detection and diagnosis of disease. (2006) — 399 citations
• Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG (2009) — 314 citations
• Recipes for the linear analysis of EEG (2005) — 602 citations