官方服务：
资源简介：
The code of paper " Deep Q-Network Enabled Low Complexity Beam Alignment"
应用场景：
创建时间：
2025-02-17
相关数据集
LSTM and CNN Parameter Tuning results
The published data are part of the virtual-coach Table Tennis shadow-play training system developing results. Two deep models (LSTM and 2DCNN) were trained and tested based on the self-collected Table
DataCite Commons2025-05-01 更新170
Additional file 5 of Interpretation of convolutional neural networks reveals crucial sequence features involving in transcription during fiber development
Additional file 5. Title of data: Accuracies of models with different parameters in SCW. Description of data: We tested performance of models with various parameters (filter number, kernel size and ma
Figshare2022-03-16 更新80
MATHEMATICAL ANALYSIS OF CONVERGENCE FOR OPTIMIZATION ALGORITHMS IN NEURAL NETWORK TRAINING.
This paper presents a rigorous mathematical analysis of the convergence properties of key optimization algorithms used in neural network training. The study investigates the dynamics of Gradient Desce
DataCite Commons2026-05-06 更新30
A Deep learning based approach for the Inverse Design of the Helmholtz resonators
Data and codes
NIAID Data Ecosystem90
Classification report of Enhanced DenseNet.
Brain tumors pose significant global health concerns due to their high mortality rates and limited treatment options. These tumors, arising from abnormal cell growth within the brain, exhibits various
NIAID Data Ecosystem30
