Artificial neural networks are algorithms that can be used to perform nonlinear statistical
modeling and provide a new alternative to logistic regression, the most commonly used …

The backpropagation learning method has opened a way to wide applications of neural
network research. It is a type of the stochastic descent method known in the sixties. The …

Page 1 清华大学信息科学技术学院教材 ——自动化系列 新编《信息,控制与系统》系列教材
Artificial Neural Networks and Evolutionary Computing 人工神经网络与模拟进化计算 (第2版) 阎 …

Learning exists in the context of data, yet notions of confidence typically focus on model
predictions, not label quality. Confident learning (CL) is an alternative approach which …

In this study, we systematically investigate the impact of class imbalance on classification
performance of convolutional neural networks (CNNs) and compare frequently used …

In recent years, deep learning has proven to be a viable methodology for surrogate
modeling and uncertainty quantification for a vast number of physical systems. However, in …

We present the automated techniques we have developed for new software that optimally
detects, deblends, measures and classifies sources from astronomical images: SExtractor …

This book provides the first comprehensive treatment of feed-forward neural networks from
the perspective of statistical pattern recognition. After introducing the basic concepts of …

In the first part of this paper, a regular recurrent neural network (RNN) is extended to a
bidirectional recurrent neural network (BRNN). The BRNN can be trained without the …

This paper introduces the problem of combining multiple partitionings of a set of objects into
a single consolidated clustering without accessing the features or algorithms that …