Edward Y. Chang

Artificial Intelligence · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2001–2011

A researcher in the Science corpus: 7 papers; trusts 36 researchers and is trusted by 93 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 30 citations into it; #4,158 of 35,345 by trust here, #8,157 by citations
• Computer Vision and Pattern Recognition — 18 citations into it; #3,171 of 21,575 by trust here, #5,754 by citations
• Computer Networks and Communications — 12 citations into it
• Optimization & Training — 8 citations into it
• Statistical Learning Theory — 7 citations into it
• Natural Language Processing — 7 citations into it
• Scaling & Learning Theory — 7 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Edward Y. Chang trusts overall

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

1. Vladimir Naumovich Vapni
2. Thorsten Joachims
3. Simon Tong
4. Leo Breiman
5. Yuhai Wu
6. Christopher K. I. Williams
7. David D. Lewis
8. Andrew P. Bradley
9. Kobus Barnard
10. Nitesh V. Chawla

Who trusts Edward Y. Chang overall

1. Haibo He
2. Burr Settles
3. Gang Wu
4. Rukshan Batuwita
5. Ritendra Datta
6. Rehan Akbani
7. Yong Wang
8. Edwardo A. Garcia
9. Deng Cai
10. Vasile Palade

Papers from Edward Y. Chang's perspective

Ordered by how strongly the researchers Edward Y. Chang 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Support vector machine active learning for image retrieval (2001) — 1,552 citations
• Parallel Spectral Clustering in Distributed Systems (2011) — 652 citations
• CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines (2003) — 501 citations
• Pfp: parallel fp-growth for query recommendation (2008) — 532 citations
• KBA: kernel boundary alignment considering imbalanced data distribution (2005) — 394 citations
• Adaptive stream resource management using Kalman Filters (2004) — 383 citations
• Class-Boundary Alignment for Imbalanced Dataset Learning (2003) — 410 citations