Fast embedding-based graph classification with connections to kernels
-
Updated
May 6, 2020 - Python
Fast embedding-based graph classification with connections to kernels
Quantum kernel estimation with backend-matched IBM noise modeling, plus reproducible branch-transfer coherence-witness experiments executed via Qiskit Runtime on IBM Quantum hardware.
A deep learning project that builds and evaluates Convolutional Neural Network (CNN) models for classifying CIFAR-10 images, compares a custom CNN with ResNet-18, and applies hyperparameter tuning to improve model performance and generalization.
A comprehensive implementation and evaluation of three state-of-the-art object detection architectures: Faster R-CNN, YOLOv11n, and DETR on COCO 2017 and Pascal VOC 2012 datasets.
Add a description, image, and links to the feature-maps topic page so that developers can more easily learn about it.
To associate your repository with the feature-maps topic, visit your repo's landing page and select "manage topics."