Pooling biospecimens is a well accepted sampling strategy in biomedical research to reduce study cost of measuring biomarkers, and has been shown in the case of normally distributed data to yield more efficient estimation. In this paper we examine the efficiency of pooling, in the context of information matrix related to estimators of unknown parameters, when the biospecimens being pooled yield...

In this paper we analyze the problem of opinion pooling. We introduce a divergence minimization framework to solve the problem of standard opinion pooling. Our results show that various existing pooling mechanisms like LinOp and LogOp are an special case of this framework. This framework is then extended to address the problem of generalized opinion pooling. We show that this framework does sat...

Recently, dropout has seen increasing use in deep learning. For deep convolutional neural networks, dropout is known to work well in fully-connected layers. However, its effect in convolutional and pooling layers is still not clear. This paper demonstrates that max-pooling dropout is equivalent to randomly picking activation based on a multinomial distribution at training time. In light of this...

Convolutional Neural Networks (CNNs) are artificial learning systems typically based on two operations: convolution, which implements feature extraction through filtering, and pooling, dimensionality reduction. The impact of pooling in the classification performance CNNs has been highlighted several previous works, a variety alternative operators have proposed. However, only few them tackle wit...