Co-training is a semi-supervised learning paradigm which trains two learners respectively from two different views and lets the learners label some unlabeled examples for each other. In this paper, we present a new PAC analysis on co-training style algorithms. We show that the co-training process can succeed even without two views, given that the two learners have large difference, which explai...

Computer graphics offer various gadgets to enhance the reconstruction of high-order statistics that are not correctly addressed by the two-point statistics approaches. Almost all the newly developed multiple-point geostatistics (MPS) algorithms, to some extent, adapt these techniques to increase the simulation accuracy and efficiency. In this work, a scrutiny comparison between our recently dev...

Abstract   Many parameter-tuning algorithms have been proposed for training Fuzzy Wavelet Neural Networks (FWNNs). Absence of appropriate structure, convergence to local optima and low speed in learning algorithms are deficiencies of FWNNs in previous studies. In this paper, a Memetic Algorithm (MA) is introduced to train FWNN for addressing aforementioned learning lacks. Differential Evolution...

Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...

Background: Length of stay is one of the most important indicators in assessing hospital performance. A shorter stay can reduce the costs per discharge and shift care from inpatient to less expensive post-acute settings. It can lead to a greater readmission rate, better resource management, and more efficient services. Objective: This study aimed to ident...

Operator learning techniques have recently emerged as a powerful tool for maps between infinite-dimensional Banach spaces. Trained under appropriate constraints, they can also be effective in the solution operator of partial differential equations (PDEs) an entirely self-supervised manner. In this work we analyze training dynamics deep networks (DeepONets) through lens Neural Tangent Kernel the...