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Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression | Scientific Reports
Random Fourier Features
Recent advances and applications of machine learning in solid-state materials science | npj Computational Materials
Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials | Nature Communications
A new three-dimensional elastography using phase based shifted Fourier transform - ScienceDirect
Emergent Orientation Selectivity from Random Networks in Mouse Visual Cortex - ScienceDirect
Fourier Feature - an overview | ScienceDirect Topics
Remote Sensing | Free Full-Text | Hyperspectral Video Target Tracking Based on Deep Edge Convolution Feature and Improved Context Filter
Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions | Journal of Chemical Theory and Computation
Brain Sciences | Free Full-Text | The Active Segmentation Platform for Microscopic Image Classification and Segmentation
Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation | The Journal of Physical Chemistry Letters
Fourier transform of a 2D real Gabor function and its filter bank. a In... | Download Scientific Diagram
IJGI | Free Full-Text | Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features
Machine Learning Interatomic Potentials and Long-Range Physics | The Journal of Physical Chemistry A
A new three-dimensional elastography using phase based shifted Fourier transform - ScienceDirect
Applied Sciences | Free Full-Text | A Two-Stage Framework for Time-Frequency Analysis and Fault Diagnosis of Planetary Gearboxes
Neural Field Models: A mathematical overview and unifying framework
Performers: The Kernel Trick, Random Fourier Features, and Attention | Teddy Koker
Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions | Journal of Chemical Theory and Computation
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials | Journal of Chemical Theory and Computation
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments | Journal of Chemical Theory and Computation
Frontiers | Machine Learning Based Classification of Resting-State fMRI Features Exemplified by Metabolic State (Hunger/Satiety)
Properties of the spontaneous activity. A: Power spectral density of... | Download Scientific Diagram
Open-Source Machine Learning in Computational Chemistry | Journal of Chemical Information and Modeling
Remote Sensing | Free Full-Text | Inverse Synthetic Aperture LiDAR Imaging of Rough Targets under Small Rotation Angles