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In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers a clearly explainable internal architecture that can outperform the existing methods, requires…

Machine Learning · Computer Science 2019-12-09 Plamen Angelov , Eduardo Soares

Deep neural networks (DNNs) have been widely applied to solve real-world regression problems. However, selecting optimal network structures remains a significant challenge. This study addresses this issue by linking neuron selection in DNNs…

Computation · Statistics 2025-09-30 Noah Yi-Ting Hung , Li-Hsiang Lin , Vince D. Calhoun

Constrained counting is important in domains ranging from artificial intelligence to software analysis. There are already a few approaches for counting models over various types of constraints. Recently, hashing-based approaches achieve…

Artificial Intelligence · Computer Science 2017-06-14 Cunjing Ge , Feifei Ma , Tian Liu , Jian Zhang

Interpretability is having an increasingly important role in the design of machine learning algorithms. However, interpretable methods tend to be less accurate than their black-box counterparts. Among others, DNFs (Disjunctive Normal Forms)…

Machine Learning · Computer Science 2022-04-12 Fabio Aiolli , Luca Bergamin , Tommaso Carraro , Mirko Polato

Failure probability estimation problem is an crucial task in engineering. In this work we consider this problem in the situation that the underlying computer models are extremely expensive, which often arises in the practice, and in this…

Methodology · Statistics 2023-02-15 Hongji Wang , Tiexin Guo , Jinglai Li , Hongqiao Wang

Accurate and efficient power flow (PF) analysis is crucial in modern electrical networks' operation and planning. Therefore, there is a need for scalable algorithms that can provide accurate and fast solutions for both small and large scale…

Machine Learning · Computer Science 2024-02-14 Nan Lin , Stavros Orfanoudakis , Nathan Ordonez Cardenas , Juan S. Giraldo , Pedro P. Vergara

Knowledge compilation studies the trade-off between succinctness and efficiency of different representation languages. For many languages, there are known strong lower bounds on the representation size, but recent work shows that, for some…

Artificial Intelligence · Computer Science 2020-11-30 Alexis de Colnet , Stefan Mengel

This paper proposes a deep neural network (DNN)-driven framework to address the longstanding generalization challenge in adaptive filtering (AF). In contrast to traditional AF frameworks that emphasize explicit cost function design, the…

Machine Learning · Statistics 2025-08-07 Qizhen Wang , Gang Wang , Ying-Chang Liang

Physics-Informed Neural Networks (PINNs) represent a groundbreaking paradigm in scientific computing, seamlessly integrating the robust framework of deep learning with fundamental physical laws. This paper meticulously applies the standard…

Numerical Analysis · Mathematics 2026-01-19 Ahmed Aberqi , Ahmed Miloudi

Deep Neural Network (DNN) has gained unprecedented performance due to its automated feature extraction capability. This high order performance leads to significant incorporation of DNN models in different Internet of Things (IoT)…

Machine Learning · Computer Science 2020-10-09 Rahul Mishra , Hari Prabhat Gupta , Tanima Dutta

Configurable systems typically consist of reusable assets that have dependencies between each other. To specify such dependencies, feature models are commonly used. As feature models in practice are often complex, automated reasoning is…

Artificial Intelligence · Computer Science 2025-05-12 Chico Sundermann , Stefan Vill , Elias Kuiter , Sebastian Krieter , Thomas Thüm , Matthias Tichy

Deep Neural Networks (DNNs) are vulnerable to adversarial examples generated by imposing subtle perturbations to inputs that lead a model to predict incorrect outputs. Currently, a large number of researches on defending adversarial…

Computer Vision and Pattern Recognition · Computer Science 2020-01-01 Hua Wang , Jie Wang , Zhaoxia Yin

Deep neural networks (DNNs) have achieved great success in the area of computer vision. The disparity estimation problem tends to be addressed by DNNs which achieve much better prediction accuracy than traditional hand-crafted feature-based…

Computer Vision and Pattern Recognition · Computer Science 2021-10-07 Qiang Wang , Shaohuai Shi , Shizhen Zheng , Kaiyong Zhao , Xiaowen Chu

Many important physical problems, such as fluid structure interaction or conjugate heat transfer, require numerical methods that compute boundary derivatives or fluxes to high accuracy. This paper proposes a novel alternative to calculating…

Numerical Analysis · Mathematics 2018-03-12 David Wells , Jeffrey Banks

Probabilistic electricity price forecasting (PEPF) is subject of increasing interest, following the demand for proper quantification of prediction uncertainty, to support the operation in complex power markets with increasing share of…

Machine Learning · Computer Science 2024-06-11 Alessandro Brusaferri , Andrea Ballarino , Luigi Grossi , Fabrizio Laurini

Approximate computing offers promising energy efficiency benefits for error-tolerant applications, but discovering optimal approximations requires extensive design space exploration (DSE). Predicting the accuracy of circuits composed of…

Hardware Architecture · Computer Science 2026-03-20 Ondrej Vlcek , Vojtech Mrazek

Tracking and approximating data matrices in streaming fashion is a fundamental challenge. The problem requires more care and attention when data comes from multiple distributed sites, each receiving a stream of data. This paper considers…

Databases · Computer Science 2014-05-01 Mina Ghashami , Jeff M. Phillips , Feifei Li

Efficiently solving sparse linear systems $Ax=b$, where $A$ is a large, sparse, symmetric positive semi-definite matrix, is a core challenge in scientific computing, machine learning, and optimization. A major bottleneck in Gaussian…

Machine Learning · Computer Science 2025-01-31 Elfarouk Harb , Ho Shan Lam

Deep learning-based numerical schemes such as Physically Informed Neural Networks (PINNs) have recently emerged as an alternative to classical numerical schemes for solving Partial Differential Equations (PDEs). They are very appealing at…

Numerical Analysis · Mathematics 2022-05-11 A. Beguinet , V. Ehrlacher , R. Flenghi , M. Fuente , O. Mula , A. Somacal

Composite adaptive radial basis function neural network (RBFNN) control with a lattice distribution of hidden nodes has three inherent demerits: 1) the approximation domain of adaptive RBFNNs is difficult to be determined a priori; 2) only…

Systems and Control · Electrical Eng. & Systems 2021-04-23 Qiong Liu , Dongyu Li , Shuzhi Sam Ge , Zhong Ouyang