Výsledky vyhľadávania - Computational Learning Theory and Optimization

An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory Autor Pirnay, Niklas, Ulitzsch, Vincent, Wilde, Frederik, Eisert, Jens, Seifert, Jean-Pierre

“… In this work, by resorting to computational learning theory and cryptographic notions, we give a fully constructive proof that quantum computers feature a super-polynomial advantage over classical…”
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Special issue of computational mechanics on machine learning theories, modeling, and applications to computational materials science, additive manufacturing, mechanics of materials, design and optimization Autor Liu, Wing Kam, Bessa, Miguel A., Chinesta, Francisco, Li, Shaofan, Trask, Nathaniel

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An in-principle super-polynomial quantum advantage for approximating combinatorial optimization problems via computational learning theory Autor Pirnay, Niklas, Ulitzsch, Vincent, Wilde, Frederik, Eisert, Jens, Seifert, Jean-Pierre

“… In this work, by resorting to computational learning theory and cryptographic notions, we prove that quantum computers feature an in-principle super-polynomial advantage over classical computers…”
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Computational optimization of associative learning experiments Autor Melinscak, Filip, Bach, Dominik R.

“… This challenge is well exemplified in associative learning research. Associative learning theory has a rich tradition of computational modeling, resulting in a growing space…”
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The Integration of the RBL-STEM Learning Model and Graph Theory in Solving Transportation and Logistics Optimization Problems to Enhance Students' Computational Thinking Skills Autor Marsidi, Marsidi

“…Abstract This study aims to integrate the Research-Based Learning (RBL) model with the STEM approach and graph theory in solving transportation and logistics optimization problems to enhance students…”
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Computational approaches to fMRI analysis Autor Cohen, Jonathan D, Daw, Nathaniel, Engelhardt, Barbara, Hasson, Uri, Li, Kai, Niv, Yael, Norman, Kenneth A, Pillow, Jonathan, Ramadge, Peter J, Turk-Browne, Nicholas B, Willke, Theodore L

“… and increasingly large fMRI datasets. In this paper, the authors review the cutting edge of such computational analyses and discuss future opportunities and challenges…”
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Molecular free energy optimization on a computational graph Autor Cao, Xiaoyong, Tian, Pu

“… Computational graph underlies major artificial intelligence platforms and is proven to facilitate training, optimization and learning…”
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Machine Learning Orchestrating the Materials Discovery and Performance Optimization of Redox Flow Battery Autor Tang, Lina, Leung, Puiki, Xu, Qian, Flox, Cristina

“…, active learning and various generative models. The collaborative integration of ML with computational techniques and experimental methods, anchored in experimentally validated Density Functional Theory (DFT…”
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Uncertainty Based Machine Learning-DFT Hybrid Framework for Accelerating Geometry Optimization Autor Singh, Akksay, Wang, Jiaqi, Henkelman, Graeme, Li, Lei

“… Here, we present a delta method-based neural network-density functional theory (DFT) hybrid optimizer to improve the computational efficiency of geometry optimization…”
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Active Learning‐Assisted Exploration of [PO40Mo12]3− for Alzheimer's Therapy Insights Autor Fang, Lincan, Peng, Ruoxue, Xia, Luping, Zhuang, Gui‐lin

“…‐learning Bayesian Optimization (BO) and density functional theory (DFT) is employed to explore low…”
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Computational Optimizations for Machine Learning

“… “Computational Optimizations for Machine Learning” of the MDPI journal Mathematics, which cover a wide range of topics connected to the theory and applications of machine learning, neural networks and artificial intelligence…”
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Meta-Learning With Differentiable Convex Optimization Autor Lee, Kwonjoon, Maji, Subhransu, Ravichandran, Avinash, Soatto, Stefano

“…Many meta-learning approaches for few-shot learning rely on simple base learners such as nearest-neighbor classifiers…”
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Accelerating CALYPSO structure prediction by data-driven learning of a potential energy surface Autor Tong, Qunchao, Xue, Lantian, Lv, Jian, Wang, Yanchao, Ma, Yanming

“… However, they are generally restricted to small systems owing to the heavy computational cost of the underlying density functional theory (DFT…”
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Machine Learning for Computational Heterogeneous Catalysis Autor Schlexer Lamoureux, Philomena, Winther, Kirsten T., Garrido Torres, Jose Antonio, Streibel, Verena, Zhao, Meng, Bajdich, Michal, Abild‐Pedersen, Frank, Bligaard, Thomas

“… – from economics to physics. In the area of materials science and computational heterogeneous catalysis, this revolution has led to the development of scientific data repositories, as well as data mining and machine learning…”
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Advanced Computational Methods for Modeling, Prediction and Optimization—A Review Autor Krzywanski, Jaroslaw, Sosnowski, Marcin, Grabowska, Karolina, Zylka, Anna, Lasek, Lukasz, Kijo-Kleczkowska, Agnieszka

“…This paper provides a comprehensive review of recent advancements in computational methods for modeling, simulation, and optimization of complex systems in materials engineering, mechanical…”
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In-depth exploration of optoelectronic and PV characteristics in lead-free Ca3BiF3 perovskite solar cells: numerical simulations and machine learning approaches Autor Biswas, Bipul Chandra, Shimul, Asadul Islam, Alshihri, Abdulaziz A., El‑Rayyes, Ali, Khan, Mohd Taukeer, Rahman, Md. Azizur

“… This study presents an integrated computational framework that combines density functional theory (DFT…”
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Reduction of computational error by optimizing SVR kernel coefficients to simulate concrete compressive strength through the use of a human learning optimization algorithm Autor Huang, Jiandong, Sun, Yuantian, Zhang, Junfei

“… to predict and optimize the compressive strength of concrete samples. For optimization purposes, this study used a human learning optimization (HLO…”
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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning Autor Nandy, Aditya, Duan, Chenru, Taylor, Michael G, Liu, Fang, Steeves, Adam H, Kulik, Heather J

“… The review will cover the development, promise, and limitations of "traditional" computational chemistry (i.e…”
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Expensive Multiobjective Optimization by Relation Learning and Prediction Autor Hao, Hao, Zhou, Aimin, Qian, Hong, Zhang, Hu

“…Expensive multiobjective optimization problems pose great challenges to evolutionary algorithms due to their costly evaluation…”
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Min-Max Cost Optimization for Efficient Hierarchical Federated Learning in Wireless Edge Networks Autor Feng, Jie, Liu, Lei, Pei, Qingqi, Li, Keqin

“…Federated learning is a distributed machine learning technology that can protect users' data privacy, so it has attracted more and more attention in the industry and academia…”
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