Modeling of nuclear masses is important for many areas science including astrophysics, reaction modeling, and data evaluations, but accuracy challenging. This paper shows how judicious use physics knowledge---so-called feature-space engineering---in machine learning, coupled with sophisticated models theoretical uncertainties, can lead to better predictions.

We present probabilistic neural programs, a framework for program induction that permits flexible specification of both a computational model and inference algorithm while simultaneously enabling the use of deep neural networks. Probabilistic neural programs combine a computation graph for specifying a neural network with an operator for weighted nondeterministic choice. Thus, a program describ...

We present probabilistic neural programs, a framework for program induction that 1 permits flexible specification of both a computational model and inference algo2 rithm while simultaneously enabling the use of deep neural networks. Probabilistic 3 neural programs combine a computation graph for specifying a neural network with 4 an operator for weighted nondeterministic choice. Thus, a program...

One of the most frequently used models for classification tasks is the Probabilistic Neural Network. Several improvements of the Probabilistic Neural Network have been proposed such as the Evolutionary Probabilistic Neural Network that employs the Particle Swarm Optimization stochastic algorithm for the proper selection of its spread (smoothing) parameters and the prior probabilities. To furthe...

By viewing noise as a resource rather than as an impediment, we demonstrate an entirely novel approach to ultra low-energy computing. The subject of this study is the probabilistic inverter, ubiquitous to the design of digital systems, whose behavior is rendered probabilistic by noise. Summarized through the concept of an energyprobability relationship for inverters based on AMI 0.5μm and TSMC ...

Probabilistic Neural Network (PNN) also termed to be a learning machine is preliminarily used with an extension of various image classifications based on Training networks and Testing networks. To efficiently detect Brain Tumor cells, clustering method based on FCM can also be implemented. The Probabilistic Neural Network (PNN) will be employed to classify the various stages of Tumor cut levels...

In this paper, we present a new approach to align sentences in bilingual parallel corpora based on a probabilistic neural network (P-NNT) classifier. A feature parameter vector is extracted from the text pair under consideration. This vector contains text features such as length, punctuation score, and cognate score values. A set of manually aligned training data was used to train the probabili...

In this paper, the structural damage localization on a simple composite plate specimen is identified using probabilistic neural networks. First, the category to be identified is defined according to the structural location, and the number of categories is reduced by grouping neighboring elements to one category. Second, the state data of damaged structure are collected by a data collection syst...

Network structure determination is an important issue in pattern classification based on a probabilistic neural network. In this study, a supervised network structure determination algorithm is proposed. The proposed algorithm consists of two parts and runs in an iterative way. The first part identifies an appropriate smoothing parameter using a genetic algorithm, while the second part determin...