Emergent Orientation Selectivity from Random Networks in Mouse Visual Cortex - ScienceDirect
Analysis of convolutional neural networks reveals the computational properties essential for subcortical processing of facial expression | Scientific Reports
Performers: The Kernel Trick, Random Fourier Features, and Attention | Teddy Koker
Brain Sciences | Free Full-Text | The Active Segmentation Platform for Microscopic Image Classification and Segmentation
Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions | Journal of Chemical Theory and Computation
neurolib: a simulation framework for whole-brain neural mass modeling | bioRxiv
Ab Initio Static Exchange–Correlation Kernel across Jacob's Ladder without Functional Derivatives | Journal of Chemical Theory and Computation
Fourier Feature - an overview | ScienceDirect Topics
Extracting dynamical understanding from neural-mass models of mouse cortex | bioRxiv
Neural Field Models: A mathematical overview and unifying framework
Fully First-Principles Surface Spectroscopy with Machine Learning | The Journal of Physical Chemistry Letters
Embedded Atom Neural Network Potentials: Efficient and Accurate Machine Learning with a Physically Inspired Representation | The Journal of Physical Chemistry Letters
Open-Source Machine Learning in Computational Chemistry | Journal of Chemical Information and Modeling
Infrared spectroscopy data- and physics-driven machine learning for characterizing surface microstructure of complex materials | Nature Communications
Flexible integration of continuous sensory evidence in perceptual estimation tasks | PNAS
Random Fourier Features
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments | Journal of Chemical Theory and Computation
Applied Sciences | Free Full-Text | A Two-Stage Framework for Time-Frequency Analysis and Fault Diagnosis of Planetary Gearboxes
The divisive normalization model of V1 neurons: a comprehensive comparison of physiological data and model predictions | Journal of Neurophysiology
Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials | Journal of Chemical Theory and Computation
Modeling Electronic Response Properties with an Explicit-Electron Machine Learning Potential | Journal of Chemical Theory and Computation
Naoki's Scientific Contributions
Flexible Dual-Branched Message-Passing Neural Network for a Molecular Property Prediction | ACS Omega
A new three-dimensional elastography using phase based shifted Fourier transform - ScienceDirect
arXiv:2110.08059v3 [cs.CV] 17 Mar 2022
Regularized by Physics: Graph Neural Network Parametrized Potentials for the Description of Intermolecular Interactions | Journal of Chemical Theory and Computation
Remote Sensing | Free Full-Text | Inverse Synthetic Aperture LiDAR Imaging of Rough Targets under Small Rotation Angles
