AZoRobotics on MSN

Neuron-driven control could redefine how soft robots handle the unexpected

This study presents a bio-inspired control framework for soft robots, enhancing tracking accuracy by over 44% under ...
IEEE

Leveraging Simplex Gradient Variance and Bias Reduction for Black-Box Optimization of Noisy and Costly Functions

Gradient variance errors in gradient-based search methods are largely mitigated using momentum, however the bias gradient errors may fail the numerical search methods in reaching the true optimum. We ...
IEEE

Federated Function-on-function Regression with an Efficient Gradient Boosting Algorithm for Privacy-Preserving Telemedicine

Abstract: Federated Learning (FL) is an emerging computing paradigm to collaboratively train Machine Learning (ML) models across multi-source data while preserving privacy. The major challenge of ...

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification BEIJING, Feb.06, 2026––WiMi Hologram Cloud Inc. (NASDAQ: WiMi) ("WiMi" or the ...
Scientific Research Publishing

Alzheimer’s Disease: Modeling the Effect of Daily Steps ()

Gregory, J.M. (2026) Alzheimer’s Disease: Modeling the Effect of Daily Steps . Journal of Behavioral and Brain Science, 16, ...
AZoOptics on MSN

End-to-end differentiable design of geometric waveguide displays

A novel differentiable approach optimizes geometric waveguide coatings, achieving substantial gains in light efficiency and uniformity for optical AR displays.
GitHub

This is the official PyTorch implementation of our paper "Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions" (CVPR 2023).

Most existing point cloud upsampling methods have roughly three steps: feature extraction, feature expansion and 3D coordinate prediction. However, they usually suffer from two critical issues: (1) ...
Scientific Research Publishing

Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics, 29, 1189-1232.

ABSTRACT: Accurate prediction of survey response rates is essential for optimizing survey design and ensuring high-quality data collection. Traditional methods often struggle to capture the complexity ...