Te-Won Lee

Signal Processing · 1999–2007

A researcher in the Science corpus: 12 papers; trusts 27 researchers and is trusted by 26 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 25 citations into it; #990 of 7,572 by trust here, #748 by citations
• Artificial Intelligence — 16 citations into it; #13,247 of 35,345 by trust here, #17,776 by citations
• Optimization & Training — 14 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it; #9,704 of 21,575 by trust here, #8,296 by citations
• Representation & Self-Supervised Learning — 10 citations into it
• Cognitive Neuroscience — 9 citations into it; #6,984 of 10,852 by trust here, #8,363 by citations
• Analytical Chemistry — 6 citations into it; #234 of 1,254 by trust here, #338 by citations
• Probabilistic & Bayesian Methods — 6 citations into it

Whom Te-Won Lee trusts overall

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

1. Shun-ichi Amari
2. Anthony J. Bell
3. Terrence J. Sejnowski
4. Aapo Hyvärinen
5. Sargur N. Srihari
6. Michael S. Lewicki
7. Geoffrey E. Hinton
8. Zoubin Ghahramani
9. Ella Bingham
10. David J. Field

Who trusts Te-Won Lee overall

1. Arnaud Delorme
2. Scott Makeig
3. Soo-Young Lee
4. Seungjin Choi
5. Yuanqing Li
6. Jian Zhou
7. Özgür Yilmaz
8. Elisa Magosso
9. Andrzej S. Cichocki
10. Jose M. P. Nascimento

Papers from Te-Won Lee's perspective

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

Their papers

• Dictionary Learning Algorithms for Sparse Representation (2003) — 932 citations
• On the multivariate Laplace distribution (2006) — 318 citations
• A Unifying Information-Theoretic Framework for Independent Component Analysis (2000) — 429 citations
• Unsupervised image classification, segmentation, and enhancement using ICA mixture models (2002) — 144 citations
• Blind Source Separation Exploiting Higher-Order Frequency Dependencies (2007) — 468 citations
• Independent Vector Analysis: An Extension of ICA to Multivariate Components (2006) — 332 citations
• Single-channel signal separation using time-domain basis functions (2003) — 67 citations
• Emotion recognition by speech signals (2003) — 418 citations
• Comparison of machine learning and traditional classifiers in glaucoma diagnosis (2002) — 215 citations
• Imaging brain dynamics using independent component analysis (2001) — 619 citations
• Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources (1999)
• Blind source separation of more sources than mixtures using overcomplete representations (1999)