J. Lo

发表

Adaptive vs. accommodative neural networks for adaptive system identification

J. Lo, D. Bassu, 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

Exponential Fourier Densities on $SO( 3 )$ and Optimal Estimation and Detection for Rotational Processes

Linda R. Eshleman, J. Lo, 1979 .

Exponential Fourier densities on SO(3) and optimal estimation and detection for rotational processes

Linda R. Eshleman, J. Lo, 1976, 1976 IEEE Conference on Decision and Control including the 15th Symposium on Adaptive Processes.

Characterizing Fourier series representation of probability distributions on compact Lie groups

J. Lo, S. Ng, 1988 .

A new approach to global optimization and its applications to neural networks

J. Lo, 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

Estimation for Rotational Processes with One Degree of Freedom

A. Willsky, J. Lo, 1972 .

Stochastic Control of Rotational Processes with One Degree of Freedom

A. Willsky, J. Lo, 1975 .

Estimation for rotational processes with one degree of freedom--Part I: Introduction and continuous-

A. Willsky, J. Lo, 1975 .

Avoiding Poor Local Minima in Training Multilayer Perceptrons ∗

J. Lo, D. Bassu, 2001 .

Convexification and Deconvexification for Training Neural Networks

J. Lo, Yun Peng, Yichuan Gui, 2016 .

Universal neuroapproximation of dynamic systems for robust identification

J. Lo, J. Lo, 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

Multilayer perceptrons and radial basis functions are universal robust approximators

James Ting-Ho Lo, J. Lo, J. Lo, 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

Global Bilinearization of Systems with Control Appearing Linearly

J. Lo, 1975 .

Differentiating and pruning multilayer feedforward neural networks

Lei Yu, James Ting-Ho Lo, J. Lo, 1995 .

Adopting Robustness and Optimality in Fitting and Learning pdf

Tim Oates, Zhiguang Wang, James Lo, 2015, ArXiv.

The normalized risk-averting error criterion for avoiding nonglobal local minima in training neural networks

Yun Peng, James Ting-Ho Lo, Yichuan Gui, 2015, Neurocomputing.

Unsupervised Hebbian learning by recurrent multilayer neural networks for temporal hierarchical pattern recognition

James Ting-Ho Lo, J. Lo, 2010, 2010 44th Annual Conference on Information Sciences and Systems (CISS).

Exponential Fourier densities and optimal estimation for axial processes

James Ting-Ho Lo, Linda R. Eshleman, J. Lo, 1979, IEEE Trans. Inf. Theory.

Exponential Fourier densities on S2 and optimal estimation and detection for directional processes

James Ting-Ho Lo, Linda R. Eshleman, J. Lo, 1977, IEEE Trans. Inf. Theory.

Adaptive Capability of Recurrent Neural Networks with Fixed Weights for Series-Parallel System Identification

James Ting-Ho Lo, J. Lo, 2009, Neural Computation.

Finite-dimensional sensor orbits and optimal nonlinear filtering

James Ting-Ho Lo, J. Lo, 1972, IEEE Trans. Inf. Theory.

Functional model of biological neural networks

James Ting-Ho Lo, J. Lo, 2010, Cognitive Neurodynamics.

A cortex-like learning machine for temporal hierarchical pattern clustering, detection, and recognition

James Ting-Ho Lo, J. Lo, 2012, Neurocomputing.

A Low-Order Model of Biological Neural Networks

James Ting-Ho Lo, J. Lo, Huachuan Wang, 2011, Neural Computation.

Exponential Fourier densities and optimal estimation and detection on the circle

James Ting-Ho Lo, J. Lo, 1977, IEEE Trans. Inf. Theory.

Neural network approach to optimal filtering

James Ting-Ho Lo, J. Lo, 1995 .

Optimal estimation for the satellite attitude using star tracker measurements

J. T.-H. Lo, J. Lo, 1986, Autom..

Mathematical justification of recurrent neural networks with long- and short-term memories

James T. Lo, Devasis Bassu, J. Lo, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

Adaptively Solving the Local-Minimum Problem for Deep Neural Networks pdf

James Ting-Ho Lo, Huachuan Wang, J. Lo, 2020, ArXiv.

Synthetic approach to optimal filtering

James Ting-Ho Lo, J. Lo, 1994, IEEE Trans. Neural Networks.

Nonlinear Dynamical Systems: Feedforward Neural Network Perspectives

Shigeru Katagiri, Simon Haykin, Jose C. Principe, 2001 .

On optimal nonlinear estimation - Part II: Discrete observation

James Ting-Ho Lo, J. Lo, 1970 .

A general bayes rule and its application to nonlinear estimation

James Ting-Ho Lo, J. Lo, 1975, Inf. Sci..

On optimal nonlinear estimation -- Part II: Discrete observation

James Ting-Ho Lo, J. Lo, 1974, Inf. Sci..

On optimal nonlinear estimation part I: Continuous observation

James Ting-Ho Lo, J. Lo, 1973, Inf. Sci..

OPTIMAL ORTHOGONAL EXPANSION FOR ESTIMAT ION I: SIGNAL IN WHITE

James Ting-Ho Lo, J. Lo, S. Ng, 1983 .

A pairwise algorithm for training multilayer perceptrons with the normalized risk-averting error criterion

Yun Peng, James Ting-Ho Lo, Yichuan Gui, 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

Existence and uniqueness of risk-sensitive estimates

James T. Lo, Thomas Wanner, J. Lo, 2002, IEEE Trans. Autom. Control..

Recursive direction finding in the presence of sensor array uncertainties

James Ting-Ho Lo, Joseph H. Clements, J. Lo, 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

Low-Order Model of Biological Neural Networks

J. Lo, Huachuan Wang, 2020, ArXiv.

Optimal Orthogonal Expansion for Estimation I: Signal in White Gaussian Noise

James Ting-Ho Lo, J. Lo, Ng Sze-Kui, 1983 .

Signal detection for bilinear systems

James Ting-Ho Lo, J. Lo, 1975, Inf. Sci..

Signal Detection on Lie Groups

James Ting-Ho Lo, J. Lo, 1973 .

New maximum likelihood approach to multiple signal estimation

James Ting-Ho Lo, J. Lo, 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

Training deep neural networks with gradual deconvexification

Yun Peng, James Ting-Ho Lo, Yichuan Gui, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).