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 | 2012 | |
|---|---|---|
| i1 |      | Xiong Li, Tai Sing Lee, Yuncai Liu: Stochastic Feature Mapping for PAC-Bayes Classification. CoRR abs/1204.2609 (2012) |
| 2011 | ||
| c11 | | Xiong Li, Tai Sing Lee, Yuncai Liu: Hybrid generative-discriminative classification using posterior divergence. CVPR 2011: 2713-2720 |
| 2010 | ||
| j11 | | Ryan C. Kelly, Matthew A. Smith, Robert E. Kass, Tai Sing Lee: Local field potentials indicate network state and account for neuronal response variability. Journal of Computational Neuroscience 29(3): 567-579 (2010) |
| c10 | | Ryan C. Kelly, Matthew A. Smith, Robert E. Kass, Tai Sing Lee: Accounting for network effects in neuronal responses using L1 regularized point process models. NIPS 2010: 1099-1107 |
| 2009 | ||
| j10 | | Thomas S. Stepleton, Zoubin Ghahramani, Geoffrey J. Gordon, Tai Sing Lee: The Block Diagonal Infinite Hidden Markov Model. Journal of Machine Learning Research - Proceedings Track 5: 552-559 (2009) |
| 2008 | ||
| j9 | | Brian Potetz, Tai Sing Lee: Efficient belief propagation for higher-order cliques using linear constraint nodes. Computer Vision and Image Understanding 112(1): 39-54 (2008) |
| 2006 | ||
| c9 | | Jason M. Samonds, Brian Potetz, Tai Sing Lee: Neurophysiological Evidence of Cooperative Mechanisms for Stereo Computation. NIPS 2006: 1201-1208 |
| 2005 | ||
| j8 | | Yuguo Yu, Tai Sing Lee: Adaptive contrast gain control and information maximization. Neurocomputing 65-66: 111-116 (2005) |
| c8 | | |
| c7 | | Thomas S. Stepleton, Tai Sing Lee: Using Co-Occurrence and Segmentation to Learn Feature-Based Object Models from Video. WACV/MOTION 2005: 129-134 |
| 2003 | ||
| j7 | | Richard Romero, Yuguo Yu, Pedram Afshar, Tai Sing Lee: Adaptation of the temporal receptive fields of macaque V1 neurons. Neurocomputing 52-54: 135-140 (2003) |
| j6 | | Yuguo Yu, Tai Sing Lee: Adaptation of the transfer function of the Hodgkin-Huxley (HH) neuronal model. Neurocomputing 52-54: 441-445 (2003) |
| c6 | | Ryan C. Kelly, Tai Sing Lee: Decoding V1 Neuronal Activity using Particle Filtering with Volterra Kernels. NIPS 2003 |
| 2002 | ||
| j5 | | Richard Romero, Tai Sing Lee: Spike train analysis for single trial data. Neurocomputing 44-46: 597-603 (2002) |
| j4 | | Gustavo Deco, Tai Sing Lee: A unified model of spatial and object attention based on inter-cortical biased competition. Neurocomputing 44-46: 775-781 (2002) |
| 2001 | ||
| c5 | | Stella X. Yu, Tai Sing Lee, Takeo Kanade: A Hierarchical Markov Random Field Model for Figure-Ground Segregation. EMMCVPR 2001: 118-133 |
| 2000 | ||
| j3 | | Stella X. Yu, Tai Sing Lee: What do V1 neurons tell us about saccadic suppression? Neurocomputing 32-33: 271-277 (2000) |
| j2 | | Elise Cassidente, Xiaogang Yan, Tai Sing Lee: A Bayesian decision approach to evaluate local and contextual information in spike trains. Neurocomputing 32-33: 1013-1020 (2000) |
| 1999 | ||
| c4 | | Tai Sing Lee, Stella X. Yu: An Information-Theoretic Framework for Understanding Saccadic Eye Movements. NIPS 1999: 834-840 |
| 1996 | ||
| j1 | | Tai Sing Lee: Image Representation Using 2D Gabor Wavelets. IEEE Trans. Pattern Anal. Mach. Intell. 18(10): 959-971 (1996) |
| 1995 | ||
| c3 | | Song Chun Zhu, Tai Sing Lee, Alan L. Yuille: Region Competition: Unifying Snakes, Region Growing, Energy/Bayes/MDL for Multi-band Image Segmentation. ICCV 1995: 416- |
| 1994 | ||
| c2 | | |
| 1992 | ||
| c1 | | Tai Sing Lee, David Mumford, Alan L. Yuille: Texture Segmentation by Minimizing Vector-Valued Energy Functionals: The Coupled-Membrane Model. ECCV 1992: 165-173 |
Colors in the list of coauthors
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