| 2009 | ||
|---|---|---|
| 38 | Jiucang Hao, Hagai Attias, Srikantan S. Nagarajan, Te-Won Lee, Terrence J. Sejnowski: Speech Enhancement, Gain, and Noise Spectrum Adaptation Using Approximate Bayesian Estimation. IEEE Transactions on Audio, Speech & Language Processing 17(1): 24-37 (2009) | |
| 2007 | ||
| 37 | Jong-Hwan Lee, Te-Won Lee, Ferenc A. Jolesz, Seung-Schik Yoo: Multivariate Analysis of fMRI Group Data Using Independent Vector Analysis. ICA 2007: 633-640 | |
| 36 | Intae Lee, Te-Won Lee: On Modelling the Frequency Components of Speech with Norm-Invariant Joint Densities. ISCAS 2007: 2982-2985 | |
| 35 | Taesu Kim, Hagai Thomas Attias, Soo-Young Lee, Te-Won Lee: Blind Source Separation Exploiting Higher-Order Frequency Dependencies. IEEE Transactions on Audio, Speech & Language Processing 15(1): 70-79 (2007) | |
| 34 | Intae Lee, Te-Won Lee: On the Assumption of Spherical Symmetry and Sparseness for the Frequency-Domain Speech Model. IEEE Transactions on Audio, Speech & Language Processing 15(5): 1521-1528 (2007) | |
| 33 | Intae Lee, Taesu Kim, Te-Won Lee: Fast fixed-point independent vector analysis algorithms for convolutive blind source separation. Signal Processing 87(8): 1859-1871 (2007) | |
| 2006 | ||
| 32 | Duangmanee Putthividhya, Te-Won Lee: Motion Patterns: High-Level Representation of Natural Video Sequences. CVPR (2) 2006: 1908-1915 | |
| 31 | Taesu Kim, Torbjørn Eltoft, Te-Won Lee: Independent Vector Analysis: An Extension of ICA to Multivariate Components. ICA 2006: 165-172 | |
| 30 | Intae Lee, Taesu Kim, Te-Won Lee: Complex FastIVA: A Robust Maximum Likelihood Approach of MICA for Convolutive BSS. ICA 2006: 625-632 | |
| 29 | Torbjørn Eltoft, Taesu Kim, Te-Won Lee: Multivariate Scale Mixture of Gaussians Modeling. ICA 2006: 799-806 | |
| 28 | Hyun-Jin Park, Te-Won Lee: Capturing nonlinear dependencies in natural images using ICA and mixture of Laplacian distribution. Neurocomputing 69(13-15): 1513-1528 (2006) | |
| 2005 | ||
| 27 | Suman K. Mitra, Te-Won Lee, Michael H. Goldbaum: A Bayesian network based sequential inference for diagnosis of diseases from retinal images. Pattern Recognition Letters 26(4): 459-470 (2005) | |
| 26 | Sooyong Choi, Te-Won Lee, Daesik Hong: Adaptive error-constrained method for LMS algorithms and applications. Signal Processing 85(10): 1875-1897 (2005) | |
| 25 | Kaisheng Yao, Kuldip K. Paliwal, Te-Won Lee: Generative factor analyzed HMM for automatic speech recognition. Speech Communication 45(4): 435-454 (2005) | |
| 2004 | ||
| 24 | Hyun-Jin Park, Te-Won Lee: A Hierarchical ICA Method for Unsupervised Learning of Nonlinear Dependencies in Natural Images. ICA 2004: 1253-1261 | |
| 23 | Hyun-Jin Park, Te-Won Lee: Modeling Nonlinear Dependencies in Natural Images using Mixture of Laplacian Distribution. NIPS 2004 | |
| 2003 | ||
| 22 | Te-Won Lee, Jean-François Cardoso, Erkki Oja, Shun-ichi Amari: Introduction to Special Issue on Independent Components Analysis. Journal of Machine Learning Research 4: 1175-1176 (2003) | |
| 21 | Gil-Jin Jang, Te-Won Lee: A Maximum Likelihood Approach to Single-channel Source Separation. Journal of Machine Learning Research 4: 1365-1392 (2003) | |
| 20 | Kenneth Kreutz-Delgado, Joseph F. Murray, Bhaskar D. Rao, Kjersti Engan, Te-Won Lee, Terrence J. Sejnowski: Dictionary Learning Algorithms for Sparse Representation. Neural Computation 15(2): 349-396 (2003) | |
| 19 | Eizaburo Doi, Toshio Inui, Te-Won Lee, Thomas Wachtler, Terrence J. Sejnowski: Spatiochromatic Receptive Field Properties Derived from Information-Theoretic Analyses of Cone Mosaic Responses to Natural Scenes. Neural Computation 15(2): 397-417 (2003) | |
| 18 | Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski: Variational Bayesian Learning of ICA with Missing Data. Neural Computation 15(8): 1991-2011 (2003) | |
| 17 | Erik M. Visser, Manabu Otsuka, Te-Won Lee: A spatio-temporal speech enhancement scheme for robust speech recognition in noisy environments. Speech Communication 41(2-3): 393-407 (2003) | |
| 2002 | ||
| 16 | Gil-Jin Jang, Te-Won Lee: A Probabilistic Approach to Single Channel Blind Signal Separation. NIPS 2002: 1173-1180 | |
| 15 | Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski: Handling Missing Data with Variational Bayesian Learning of ICA. NIPS 2002: 881-888 | |
| 14 | Te-Won Lee, Michael S. Lewicki: Unsupervised image classification, segmentation, and enhancement using ICA mixture models. IEEE Transactions on Image Processing 11(3): 270-279 (2002) | |
| 13 | Kwokleung Chan, Te-Won Lee, Terrence J. Sejnowski: Variational Learning of Clusters of Undercomplete Nonsymmetric Independent Components. Journal of Machine Learning Research 3: 99-114 (2002) | |
| 12 | Jong-Hwan Lee, Te-Won Lee, Ho-Young Jung, Soo-Young Lee: On the Efficient Speech Feature Extraction Based on Independent Component Analysis. Neural Processing Letters 15(3): 235-245 (2002) | |
| 11 | Shun-ichi Amari, Aapo Hyvärinen, Soo-Young Lee, Te-Won Lee, V. David Sánchez A.: Blind signal separation and independent component analysis. Neurocomputing 49(1-4): 1-5 (2002) | |
| 10 | Gil-Jin Jang, Te-Won Lee, Yung-Hwan Oh: Learning statistically efficient features for speaker recognition. Neurocomputing 49(1-4): 329-348 (2002) | |
| 2000 | ||
| 9 | Te-Won Lee, Thomas Wachtler, Terrence J. Sejnowski: The Spectral Independent Components of Natural Scenes. Biologically Motivated Computer Vision 2000: 527-534 | |
| 8 | Te-Won Lee, Thomas Wachtler, Terrence J. Sejnowski: Color Opponency Constitutes a Sparse Representation for the Chromatic Structure of Natural Scenes. NIPS 2000: 866-872 | |
| 7 | Te-Won Lee, Michael S. Lewicki, Terrence J. Sejnowski: ICA Mixture Models for Unsupervised Classification of Non-Gaussian Classes and Automatic Context Switching in Blind Signal Separation. IEEE Trans. Pattern Anal. Mach. Intell. 22(10): 1078-1089 (2000) | |
| 1999 | ||
| 6 | Te-Won Lee: Independent component analysis: theory and applications [Book Review]. IEEE Transactions on Neural Networks 10(4): 982-982 (1999) | |
| 5 | Te-Won Lee, Mark Girolami, Terrence J. Sejnowski: Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Sub-Gaussian and Super-Gaussian Sources. Neural Computation 11(2): 417-441 (1999) | |
| 1998 | ||
| 4 | Te-Won Lee, Michael S. Lewicki, Terrence J. Sejnowski: Unsupervised Classification with Non-Gaussian Mixture Models Using ICA. NIPS 1998: 508-514 | |
| 1997 | ||
| 3 | Bert-Uwe Koehler, Te-Won Lee, Reinhold Orglmeister: Improving the Performance of Infomax Using Statistical Signal Processing Techniques. ICANN 1997: 535-540 | |
| 2 | Tzyy-Ping Jung, Colin Humphries, Te-Won Lee, Scott Makeig, Martin J. McKeown, Vicente Iragui, Terrence J. Sejnowski: Extended ICA Removes Artifacts from Electroencephalographic Recordings. NIPS 1997 | |
| 1996 | ||
| 1 | Te-Won Lee, Anthony J. Bell, Russell H. Lambert: Blind Separation of Delayed and Convolved Sources. NIPS 1996: 758-764 | |