报告题目: A Cortex-Like Learning Machine for Temporal Hierarchical Pattern Clustering, Detection, and Recognition
报告人: Prof. James Ting-Ho Lo Department of Mathematics and Statistics
University of Maryland Baltimore County
邀请人: 兰旭光副教授 新葡萄8883官网AMG电信学院 人工智能与机器人研究所
报告时间: 10:00-11:30am 6月7日 (周二)
报告地点: 逸夫科学馆 西安交大人机所 324室
（报告后有座谈,欢迎大家参加!）

报告内容:
介绍了一种新的机器学习范例,即一种低阶生物神经网络模型.
A new paradigm of machine learning, which is also a Low-Order Model (LOM) of biological neural networks, will be proposed. LOM is a network of biologically plausible models of dendritic nodes/trees, spiking/nonspiking neurons, unsupervised/supervised covariance/accumulation learning mechanisms, feedback connections, and a scheme for maximal generalization.
These component models were motivated and necessitated by making LOM learn and retrieve easily; and cluster, detect and recognize multiple/hierarchical corrupted, distorted and occluded temporal and spatial patterns.
On one hand, with many features and capabilities desirable of a learning machine, LOM is expected to be a powerful engine for intelligent systems. On the other hand, biological plausibility of LOM makes a strong case that LOM is the common cortical algorithm long hypothesized by neuroscientists.

报告人简介:
James Ting-Ho Lo is a Professor in the Department of Mathematics and Statistics, University of Maryland Baltimore County, Baltimore, Maryland, USA. He received the Ph.D. degree from the University of Southern California and was a Postdoctoral Research Associate at Stanford University and Harvard University. His research interests have included optimal filtering, system control and identification, active noise and vibration control, and computational intelligence. In 1992, he solved the long-standing notorious problem of optimal nonlinear filtering in its most general setting and obtained a best paper award.
Subsequently, he conceived and developed adaptive neural networks with long- and short-term memories, accommodative neural network for adaptive processing without online processor adjustment, and robust/adaptive neural networks with a continuous spectrum of robustness; which constitute a systematic general approach to effective robust or/and adaptive processing for system control/identification and signal processing.
He developed the convexification method for avoiding poor local-minima in data fitting (e.g., training neural networks and estimating regression models), removing a main obstacle in the neural network approach and nonlinear regression in statistics.