arXiv:2110.08059v3 [cs.CV] 17 Mar 2022
Applied Sciences | Free Full-Text | Phase Extraction from Single Interferogram Including Closed-Fringe Using Deep Learning | HTML
Transferable Multilevel Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multitask Learning | Journal of Chemical Information and Modeling
Electronic Configurations of 3d Transition-Metal Compounds Using Local Structure and Neural Networks | The Journal of Physical Chemistry A
Machine Learning Force Fields | Chemical Reviews
Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential | Journal of Chemical Theory and Computation
Sensors | Free Full-Text | A Fringe Phase Extraction Method Based on Neural Network | HTML
Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables | The Journal of Physical Chemistry B
29th Annual Computational Neuroscience Meeting: CNS*2020 | BMC Neuroscience | Full Text
Gabor transform - Wikipedia
Neural Field Models: A mathematical overview and unifying framework
Remote Sensing | Free Full-Text | Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey | HTML
Image reconstruction by domain-transform manifold learning | Nature
Neural Field Models: A mathematical overview and unifying framework
Survey of spiking in the mouse visual system reveals functional hierarchy | Nature
A hybrid classical techniques and optimal decision model for iris recognition under variable image quality conditions | SpringerLink
27th Annual Computational Neuroscience Meeting (CNS*2018): Part One | BMC Neuroscience | Full Text
29th Annual Computational Neuroscience Meeting: CNS*2020 | BMC Neuroscience | Full Text
Spatiotemporal Gabor filter for motion processing - Explanation of parameters
Machine Learning Force Fields | Chemical Reviews
J. Imaging | Free Full-Text | Colour and Texture Descriptors for Visual Recognition: A Historical Overview | HTML
Recent advances and applications of machine learning in solid-state materials science | npj Computational Materials
Recent advances and applications of machine learning in solid-state materials science | npj Computational Materials
Estimating scattering potentials in inverse problems with Volterra series and neural networks | SpringerLink
