Topic: "softmax-function"
mahnoorsheikh16/Node-Classification-with-Graph-Neural-Networks
Evaluation of multiple graph neural network models—GCN, GAT, GraphSAGE, MPNN and DGI—for node classification on graph-structured data. Preprocessing includes feature normalization and adjacency-matrix regularization, and an ensemble of model predictions boosts performance. The best ensemble achieves 83.47% test accuracy.
Language: Jupyter Notebook - Size: 285 KB - Last synced at: 5 days ago - Pushed at: 5 days ago - Stars: 1 - Forks: 0

TahirZia-1/Neural-Networks-MNIST-in-Python
Implementations of neural networks in python for the classification of MNIST datasets.
Language: Jupyter Notebook - Size: 43.9 KB - Last synced at: 2 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

guoxxiong/mppi_local_planner
An Eigen-based ROS1 plugin for mobile robot commands planning. Model Predictive Path Integral, Normal Distribution Noise, SG Smoother, Softmax, Dynamic Reconfigure
Language: C++ - Size: 91.8 KB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 0 - Forks: 1

dmivilensky/Accelerated-Coordinate-Descent-Method
The implementation of Coordinate Descent Method Accelerated by Universal Metaalgorithm with efficient amortised complexity of iteration & Experiments with sparse SoftMax function, where the proposed method is better than FGM
Language: Jupyter Notebook - Size: 41 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 0 - Forks: 1
