Results 191 to 200 of about 149,306 (281)

Exact and parameterized algorithms for choosability. [PDF]

Acta Inform
Bliznets I, Nederlof J.
europepmc   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

QRBT: Quantum Driven Reinforcement Learning for Scalable Blockchain Transaction Processing. [PDF]

PLoS One
Lella KK, Mallu SRK.
europepmc   +1 more source

RAMS: Residual‐Based Adversarial‐Gradient Moving Sample Method for Scientific Machine Learning in Solving Partial Differential Equations

Advanced Intelligent Discovery, EarlyView.
We propose a residual‐based adversarial‐gradient moving sample (RAMS) method for scientific machine learning that treats samples as trainable variables and updates them to maximize the physics residual, thereby effectively concentrating samples in inadequately learned regions.
Weihang Ouyang, Min Zhu, Wei Xiong, Si‐Wei Liu, Lu Lu   +4 more
wiley   +1 more source

Analyzing Parameter-Efficient Convolutional Neural Network Architectures for Visual Classification. [PDF]

Sensors (Basel)
Shahadat N, Maida AS.
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Spatial and Directional Modulation Systems for Near-Field Secure Transmission. [PDF]

Sensors (Basel)
Liu J, Zhong Y, Wang Y, Gong D, Xiao Y.
europepmc   +1 more source

Parameterized Complexity and Inapproximability of Dominating Set Problem in Chordal and Near Chordal Graphs. [PDF]

J Comb Optim, 2010
Liu C, Song Y.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

DPDQN-TER: An Improved Deep Reinforcement Learning Approach for Mobile Robot Path Planning in Dynamic Scenarios. [PDF]

Sensors (Basel)
Gao S, Xu Y, Guo X, Liu C, Wang X.
europepmc   +1 more source