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James Ting-Ho Lo
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Journal Articles
- 2018
- [j20]Yu Guo, Fei Wang, James Ting-Ho Lo:
Chaotic synchronization based on neural filter. J. Frankl. Inst. 355(4): 1579-1595 (2018) - 2015
- [j19]James Ting-Ho Lo, Yichuan Gui, Yun Peng:
The normalized risk-averting error criterion for avoiding nonglobal local minima in training neural networks. Neurocomputing 149: 3-12 (2015) - 2012
- [j18]James Ting-Ho Lo:
A cortex-like learning machine for temporal hierarchical pattern clustering, detection, and recognition. Neurocomputing 78(1): 89-103 (2012) - 2011
- [j17]James Ting-Ho Lo:
A Low-Order Model of Biological Neural Networks. Neural Comput. 23(10): 2626-2682 (2011) - 2010
- [j16]James Ting-Ho Lo:
Convexification for data fitting. J. Glob. Optim. 46(2): 307-315 (2010) - [j15]James Ting-Ho Lo:
Convexification for data fitting. J. Glob. Optim. 46(2): 317-318 (2010) - 2009
- [j14]James Ting-Ho Lo:
Adaptive Capability of Recurrent Neural Networks with Fixed Weights for Series-Parallel System Identification. Neural Comput. 21(11): 3214-3227 (2009) - 1994
- [j13]James Ting-Ho Lo:
Synthetic approach to optimal filtering. IEEE Trans. Neural Networks 5(5): 803-811 (1994) - 1992
- [j12]James Ting-Ho Lo, Stanley Lawrence Marple Jr.:
Observability conditions for multiple signal direction finding and array sensor localization. IEEE Trans. Signal Process. 40(11): 2641-2650 (1992) - 1987
- [j11]James Ting-Ho Lo, Sze-Kui Ng:
Optimal functional expansion for estimation from counting observations. IEEE Trans. Inf. Theory 33(1): 21-35 (1987) - 1979
- [j10]James Ting-Ho Lo, Linda R. Eshleman:
Exponential Fourier densities and optimal estimation for axial processes. IEEE Trans. Inf. Theory 25(4): 463-470 (1979) - 1977
- [j9]James Ting-Ho Lo:
Exponential Fourier densities and optimal estimation and detection on the circle. IEEE Trans. Inf. Theory 23(1): 110-116 (1977) - [j8]James Ting-Ho Lo, Linda R. Eshleman:
Exponential Fourier densities on S2 and optimal estimation and detection for directional processes. IEEE Trans. Inf. Theory 23(3): 321-336 (1977) - 1975
- [j7]James Ting-Ho Lo:
A general bayes rule and its application to nonlinear estimation. Inf. Sci. 8(3): 189-198 (1975) - [j6]James Ting-Ho Lo:
Signal detection for bilinear systems. Inf. Sci. 9(3): 249-278 (1975) - [j5]James Ting-Ho Lo:
Representation of Continuous Curves on the 3-Dimensional Rotation Group. Math. Syst. Theory 8(4): 368-375 (1975) - 1974
- [j4]James Ting-Ho Lo:
Signal Detection of Rotational Processes and Frequency Demodulation. Inf. Control. 26(2): 99-115 (1974) - [j3]James Ting-Ho Lo:
On optimal nonlinear estimation -- Part II: Discrete observation. Inf. Sci. 7: 1-10 (1974) - 1973
- [j2]James Ting-Ho Lo:
On optimal nonlinear estimation part I: Continuous observation. Inf. Sci. 6: 19-32 (1973) - 1972
- [j1]James Ting-Ho Lo:
Finite-dimensional sensor orbits and optimal nonlinear filtering. IEEE Trans. Inf. Theory 18(5): 583-588 (1972)
Conference and Workshop Papers
- 2017
- [c21]Yu Guo, Fei Wang, James Ting-Ho Lo:
Nonlinear system identification based on recurrent neural networks with shared and specialized memories. ASCC 2017: 2054-2059 - [c20]Yu Guo, Fei Wang, James Ting-Ho Lo:
A neural filter-based scheme for synchronizing chaotic systems. ICASSP 2017: 4666-4670 - [c19]James Ting-Ho Lo, Yichuan Gui, Yun Peng:
Solving the Local-Minimum Problem in Training Deep Learning Machines. ICONIP (1) 2017: 166-174 - 2016
- [c18]James Ting-Ho Lo, Yichuan Gui, Yun Peng:
Training deep neural networks with gradual deconvexification. IJCNN 2016: 1000-1007 - [c17]James Ting-Ho Lo, Yu Guo:
Accommodative neural filters. IJCNN 2016: 2344-2351 - 2014
- [c16]Yichuan Gui, James Ting-Ho Lo, Yun Peng:
A pairwise algorithm for training multilayer perceptrons with the normalized risk-averting error criterion. IJCNN 2014: 358-365 - 2013
- [c15]James Ting-Ho Lo, Yichuan Gui, Yun Peng:
Overcoming the local-minimum problem in training multilayer perceptrons by gradual deconvexification. IJCNN 2013: 1-6 - [c14]James Ting-Ho Lo, Yichuan Gui, Yun Peng:
Overcoming the Local-Minimum Problem in Training Multilayer Perceptrons with the NRAE-MSE Training Method. ISNN (1) 2013: 83-90 - 2012
- [c13]James Ting-Ho Lo, Yichuan Gui, Yun Peng:
Overcoming the Local-Minimum Problem in Training Multilayer Perceptrons with the NRAE Training Method. ISNN (1) 2012: 440-447 - 2011
- [c12]James Ting-Ho Lo:
A low-order model of biological neural networks for hierarchical or temporal pattern clustering, detection and recognition. IJCNN 2011: 37-44 - 2010
- [c11]James Ting-Ho Lo:
Unsupervised Hebbian learning by recurrent multilayer neural networks for temporal hierarchical pattern recognition. CISS 2010: 1-6 - 2008
- [c10]James Ting-Ho Lo:
Probabilistic associative memories. IJCNN 2008: 3895-3903 - 1997
- [c9]James Ting-Ho Lo:
Robust adaptive identification of dynamic systems by neural networks. ICNN 1997: 1121-1126 - [c8]James Ting-Ho Lo, Lei Yu:
Overcoming recurrent neural networks' compactness limitation for neurofiltering. ICNN 1997: 2181-2186 - [c7]James Ting-Ho Lo:
Robust adaptive neurofilters with or without online weight adjustment. ICNN 1997: 2245-2250 - 1996
- [c6]James Ting-Ho Lo:
Adaptive system identification by nonadaptively trained neural networks. ICNN 1996: 2066-2071 - 1993
- [c5]Joseph H. Clements III, James Ting-Ho Lo:
Recursive direction finding in the presence of sensor array uncertainties. ICASSP (4) 1993: 308-311 - 1991
- [c4]James Ting-Ho Lo:
Backestimation for training multilayer perceptrons. ICASSP 1991: 1065-1068 - 1990
- [c3]James Ting-Ho Lo:
Array sensor localization and calibration by cyclic regression. ICASSP 1990: 2939-2942 - 1988
- [c2]James Ting-Ho Lo:
New maximum likelihood approach to multiple signal estimation. ICASSP 1988: 2889-2892 - 1987
- [c1]James Ting-Ho Lo, Stanley Lawrence Marple Jr.:
Eigenstructure methods for array sensor localization. ICASSP 1987: 2260-2263
Informal and Other Publications
- 2020
- [i2]Huachuan Wang, James Ting-Ho Lo:
Low-Order Model of Biological Neural Networks. CoRR abs/2012.06720 (2020) - [i1]Huachuan Wang, James Ting-Ho Lo:
Adaptively Solving the Local-Minimum Problem for Deep Neural Networks. CoRR abs/2012.13632 (2020)
Coauthor Index
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