Pang-Ning Tan

Artificial Intelligence · 2000–2017

A researcher in the Science corpus: 10 papers; trusts 37 researchers and is trusted by 31 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 34 citations into it; #10,674 of 35,345 by trust here, #23,077 by citations
• Representation & Self-Supervised Learning — 24 citations into it
• Computational Mechanics — 16 citations into it
• Optimization & Training — 9 citations into it
• Computer Vision and Pattern Recognition — 8 citations into it; #9,096 of 21,575 by trust here, #17,757 by citations
• Algorithms & Complexity — 7 citations into it
• Computer Networks and Communications — 7 citations into it
• Information Systems — 7 citations into it; #916 of 8,771 by trust here, #1,102 by citations

Whom Pang-Ning Tan trusts overall

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

1. J. Ross Quinlan
2. David J. Hand
3. R. Caruana
4. Jiayu Zhou
5. Theodoros Evgeniou
6. Rakesh Agrawal
7. Carla E. Brodley
8. Hal Daumé Iii
9. Roberto J. Bayardo Jr.
10. Edward H. Shortliffe

Who trusts Pang-Ning Tan overall

1. Bamshad Mobasher
2. Vipin Kumar
3. Mahak Gambhir
4. Kien Pham
5. Salvatore Ruggieri
6. Hong Cheng
7. Cynthia Rudin
8. Dimitrios Pierrakos
9. Yu Zhang
10. Charu C. Aggarwal

Papers from Pang-Ning Tan's perspective

Ordered by how strongly the researchers Pang-Ning Tan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Multitask Learning
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Selecting the right interestingness measure for association patterns (2002) — 1,050 citations
• Online Multi-Task Learning Framework for Ensemble Forecasting (2017) — 29 citations
• Selecting the right objective measure for association analysis (2004) — 623 citations
• GSpartan: a Geospatio-Temporal Multi-task Learning Framework for Multi-location Prediction (2016) — 22 citations
• FORMULA: FactORized MUlti-task LeArning for task discovery in personalized medical models (2015) — 33 citations
• Introduction to Data Mining, (First Edition) (2005) — 1,614 citations
• Discovery of Web Robot Sessions Based on their Navigational Patterns (2004) — 350 citations
• Data Mining for Network Intrusion Detection (2002) — 346 citations
• Web usage mining: discovery and applications of usage patterns from Web data (2000) — 2,664 citations
• Interestingness Measures for Association Patterns : A Perspective (2000) — 228 citations