Tai Sing Lee
Orcid: 0000-0003-4483-5694
According to our database1,
Tai Sing Lee
authored at least 43 papers
between 1992 and 2024.
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Bibliography
2024
Incremental Learning and Self-Attention Mechanisms Improve Neural System Identification.
CoRR, 2024
2023
Does resistance to Style-Transfer equal Shape Bias? Evaluating Shape Bias by Distorted Shape.
CoRR, 2023
A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes.
CoRR, 2023
Emergence of Shape Bias in Convolutional Neural Networks through Activation Sparsity.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023
2022
CoRR, 2022
Proceedings of the Tenth International Conference on Learning Representations, 2022
2021
Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons.
PLoS Comput. Biol., 2021
Recurrent networks improve neural response prediction and provide insights into underlying cortical circuits.
CoRR, 2021
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021
2019
J. Comput. Neurosci., 2019
CoRR, 2019
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
2017
Proceedings of the 5th International Conference on Learning Representations, 2017
Proceedings of the 14th Conference on Computer and Robot Vision, 2017
Proceedings of the 14th Conference on Computer and Robot Vision, 2017
2015
Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction.
Proceedings of the 3rd International Conference on Learning Representations, 2015
2014
Neural Comput., 2014
2013
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013
2012
2011
Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 2011
2010
Local field potentials indicate network state and account for neuronal response variability.
J. Comput. Neurosci., 2010
Accounting for network effects in neuronal responses using L1 regularized point process models.
Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010
2009
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009
2008
Comput. Vis. Image Underst., 2008
2006
Proceedings of the Advances in Neural Information Processing Systems 19, 2006
2005
Using Co-Occurrence and Segmentation to Learn Feature-Based Object Models from Video.
Proceedings of the 7th IEEE Workshop on Applications of Computer Vision / IEEE Workshop on Motion and Video Computing (WACV/MOTION 2005), 2005
Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005
2004
Neurocomputing, 2004
2003
Neurocomputing, 2003
Neurocomputing, 2003
Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003
2002
A unified model of spatial and object attention based on inter-cortical biased competition.
Neurocomputing, 2002
2001
Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2001
2000
A Bayesian decision approach to evaluate local and contextual information in spike trains.
Neurocomputing, 2000
1999
Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999
1996
IEEE Trans. Pattern Anal. Mach. Intell., 1996
1995
Region Competition: Unifying Snakes, Region Growing, Energy/Bayes/MDL for Multi-band Image Segmentation.
Proceedings of the Procedings of the Fifth International Conference on Computer Vision (ICCV 95), 1995
1994
Proceedings of the Proceedings 1994 International Conference on Image Processing, 1994
1992
Texture Segmentation by Minimizing Vector-Valued Energy Functionals: The Coupled-Membrane Model.
Proceedings of the Computer Vision, 1992