John Paparrizos

Signal Processing · 2015–2016

A researcher in the Science corpus: 2 papers; trusts 38 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 38 citations into it; #2,242 of 7,572 by trust here, #6,083 by citations
• Economics and Econometrics — 13 citations into it; #7,070 of 23,481 by trust here, #19,308 by citations
• Artificial Intelligence — 9 citations into it; #16,291 of 35,345 by trust here, #33,294 by citations
• Computer Networks and Communications — 6 citations into it
• Statistics and Probability — 6 citations into it

Whom John Paparrizos trusts overall

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

1. Eamonn J. Keogh
2. T. Warren Liao
3. Matteo Frigo
4. Andrew Y. Ng
5. Gene H. Golub
6. Jaka Demsar
7. Chotirat (Ann) Ratanamahatana
8. Hui Ding
9. Abdullah Mueen
10. Thanawin Rakthanmanon

Who trusts John Paparrizos overall

1. Zhicheng Cui
2. Jiawei Jiang
3. Yixin Chen
4. Chengkai Han
5. Muhammad Usama
6. Luis Gravano
7. Wayne Xin Zhao
8. Jingyuan Wang
9. Junaid Qadir

Papers from John Paparrizos's perspective

Ordered by how strongly the researchers John Paparrizos 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. Locally adaptive dimensionality reduction for indexing large time series databases (ACM SIGMOD Conference)
6. Exact indexing of dynamic time warping (Knowledge and Information Systems)
7. Using Dynamic Time Warping to Find Patterns in Time Series
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• k-Shape: Efficient and Accurate Clustering of Time Series (2016) — 746 citations
• k-Shape: Efficient and Accurate Clustering of Time Series (2015) — 516 citations