Suchergebnisse - Computing methodologies Machine learning Machine learning approaches Kernel methods

Quantum Computing With Kernel Methods for Machine Learning

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INTERNATIONAL PATENT: GOOGLE LLC FILES APPLICATION FOR "QUANTUM COMPUTING WITH KERNEL METHODS FOR MACHINE LEARNING"

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Google LLC Files Patent Application for Quantum Computing with Kernel Methods for Machine Learning

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A Kernel-based Machine Learning Approach to Computing Quasiparticle Energies within Many-Body Green's Functions Theory von Tirimbó, Gianluca, Çaylak, Onur, Baumeier, Björn

“… We present a Kernel Ridge Regression (KRR) based supervised learning method combined with Genetic Algorithms (GAs …”
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US Patent Issued to Google on June 3 for "Quantum computing with kernel methods for machine learning" (California Inventors)

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Using Kernel Methods in a Learning Machine Approach for Multispectral Data Classification. An Application in Agriculture von Gonzalez, Adrian

“… Using Kernel Methods in a Learning Machine Approach for Multispectral Data Classification …”
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Polynomial chaos vs kernel machine learning methods for uncertainty quantification: A comparative study and benchmarking with the hybrid PCE-GPR approach von Manfredi, Paolo

“… Besides popular methods based on polynomial chaos expansion (PCE), recent works explored the application of machine learning regression methods to UQ, particularly …”
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Selection of a Suitable Rock Mixing Method for Computing Gardner’s Constant Through a Machine Learning (ML) Approach to Estimate the Compressional Velocity: A study from the Jaisalmer sub-basin, India von Yalamanchi, Pydiraju, Datta Gupta, Saurabh

“… The frequent variability of petrophysical properties makes hydrocarbon exploration challenging in carbonate reservoirs. Nowadays, quantitative interpretation …”
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A Novel Sequence-Based Method of Predicting Protein DNA-Binding Residues, Using a Machine Learning Approach von Cai, Yudong, Shanghai University, Shanghai, People's Republic of China, He, ZhiSong, Zhejiang University, ZheJiang, People's Republic of China, Shi, Xiaohe, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, People's Republic of China, Kong, Xiangying, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, People's Republic of China, Gu, Lei, Fraunhofer Institute for Algorithms and Scientific Computing, Germany, Xie, Lu, Shanghai Center for Bioinformation Technology, Shanghai, People's Republic of China

“… Some in silico methods have been developed, but there are still considerable limitations. In this study, we used a machine learning approach to develop a new sequence-based method of predicting protein-DNA binding residues …”
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MAGIKA: AI-Powered Content-Type Detection von Fratantonio, Yanick, Invernizzi, Luca, Farah, Loua, Thomas, Kurt, Zhang, Marina, Albertini, Ange, Galilee, Francois, Metitieri, Giancarlo, Cretin, Julien, Petit-Bianco, Alex, Tao, David, Bursztein, Elie

“… Under the hood, Magika employs a deep learning model that can execute on a single CPU with just 1MB of memory to store the model's weights …”
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Finding Ethereum Smart Contracts Security Issues by Comparing History Versions von Chen, Jiachi

“… In this paper, we propose a deep learning-based method to find security issues of Ethereum smart contracts by finding the updated version of a destructed contract …”
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LA-MTL: Latency-Aware Automated Multi-Task Learning von Sampath, Shambhavi Balamuthu, Sawani, Sami, Thoma, Moritz, Frickenstein, Lukas, Mori, Pierpaolo, Fasfous, Nael, Vemparala, Manoj Rohit, Frickenstein, Alexander, Schlichtmann, Ulf, Passerone, Claudio, Stechele, Walter

“… Multi-Task Learning (MTL) aims to unify a variety of tasks into a single network for improved training and inference efficiency …”
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When Less is Enough: Positive and Unlabeled Learning Model for Vulnerability Detection von Wen, Xin-Cheng, Wang, Xinchen, Gao, Cuiyun, Wang, Shaohua, Liu, Yang, Gu, Zhaoquan

“… Automated code vulnerability detection has gained increasing attention in recent years. The deep learning (DL …”
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Scalable Industrial Control System Analysis via XAI-Based Gray-Box Fuzzing von Kur, Justin, Chen, Jingshu, Huang, Jun

“… To address these limitations, we propose XAI-based gray-box fuzzing, a novel approach that leverages explainable AI and machine learning modeling of ICS to accurately identify a small set …”
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Function-Level Vulnerability Detection Through Fusing Multi-Modal Knowledge von Ni, Chao, Guo, Xinrong, Zhu, Yan, Xu, Xiaodan, Yang, Xiaohu

“… Software vulnerabilities damage the functionality of software systems. Recently, many deep learning-based approaches have been proposed to detect vulnerabilities at the function level by using one or a few different modalities (e.g …”
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MCBound: An Online Framework to Characterize and Classify Memory/Compute-bound HPC Jobs von Antici, Francesco, Bartolini, Andrea, Kiziltan, Zeynep, Babaoglu, Ozalp, Kodama, Yuetsu

“… Modern High-Performance Computing (HPC) systems play a fundamental role in driving scientific research, as they execute computationally intensive jobs originating from diverse domains …”
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Using Static Analysis to Aid Monolith to Microservice System Transformation: Tuning Fuzzy c-Means in a VAE-Based GNN Approach von Sooksatra, Korn, Hossain Chy, Md Showkat, Arju, Md Ashfakur Rahman, Cerny, Tomas, Rivas, Pablo

“… This paper explores a machine learning-driven approach to decompose monolithic systems into microservices, targeting maintainability and modularization …”
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Optimizing Uncertainty Estimation on Scientific Visualizations Using Learning Models von Almeida, Clara J., Guerrero-Pantoja, David, Pautsch, Erik, Rizzi, Silvio, Thiruvathukal, George K., Pantoja, Maria

“… In recent years, machine learning, particularly Deep Learning (DL), has driven significant advancements in SciVis …”
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hws: A Tool for Monitoring Hardware Metrics Across Diverse Vendors: A Case Study on Hyperparameter Optimization Algorithms von Breyer, Marcel, Van Craen, Alexander, Domanski, Peter, Pfluger, Dirk

“… Due to modern hardware's constantly growing energy demands, it is important to consider energy efficiency and power consumption. Especially in the age of AI, …”
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Semantic Segmentation of Geometric Primitives in Dense 3D Point Clouds von Stanescu, Ana, Fleck, Philipp, Schmalstieg, Dieter, Arth, Clemens

“… The core contribution is an efficient method for fitting of geometric primitives based on machine learning …”
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