Search alternatives:
modular implementation » world implementation (Expand Search)
model implementation » world implementation (Expand Search), time implementation (Expand Search), policy implementation (Expand Search)
code implementation » time implementation (Expand Search), world implementation (Expand Search), _ implementation (Expand Search)
modular implementation » world implementation (Expand Search)
model implementation » world implementation (Expand Search), time implementation (Expand Search), policy implementation (Expand Search)
code implementation » time implementation (Expand Search), world implementation (Expand Search), _ implementation (Expand Search)
-
81
-
82
-
83
-
84
-
85
-
86
-
87
Flowchart representation of lion optimization algorithm for hyperparameter tuning in the HCAP model.
Published 2025Subjects: -
88
-
89
Code
Published 2025“…We divided the dataset into training and test sets, using 70% of the genes for training and 30% for testing. We implemented machine learning algorithms using the following R packages: rpart for Decision Trees, gbm for Gradient Boosting Machines (GBM), ranger for Random Forests, the glm function for Generalized Linear Models (GLM), and xgboost for Extreme Gradient Boosting (XGB). …”
-
90
-
91
-
92
Scripts, data and figures underpinning 'Towards the Creation of Legible Octilinear Power Grid Diagrams Using Mixed Integer Linear Programming'
Published 2024“…<p dir="ltr">These Python notebooks implement the techniques described in the paper "Towards the Creation of Legible Octilinear Power Grid Diagrams Using Mixed Integer Linear Programming".…”
-
93
Code and data for reproducing the results in the original paper of DML-Geo
Published 2025“…<p dir="ltr">This asset provides all the code and data for reproducing the results (figures and statistics) in the original paper of DML-Geo</p><h2>Main Files:</h2><p dir="ltr"><b>main.ipynb</b>: the main notebook to generate all the figures and data presented in the paper</p><p dir="ltr"><b>data_generator.py</b>: used for generating synthetic datasets to validate the performance of different models</p><p dir="ltr"><b>dml_models.py</b>: Contains implementations of different Double Machine Learning variants used in this study.…”
-
94
-
95
Reproducible Code and Data for figures
Published 2025“…</i></p><p dir="ltr">It contains:</p><p dir="ltr">✅ <b>Python Code</b> – Scripts used for data preprocessing, and visualization.…”
-
96
software code of NeoDesign
Published 2024“…<h2>Implementation and Dependencies</h2><p dir="ltr">neoDesign was developed with python (recommend>3.9) and shell (bash) language. …”
-
97
DA-Faster-RCNN code
Published 2025“…The implementation is written in Python using PyTorch and Detectron2.…”
-
98
-
99
The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network"
Published 2024“…</p><h3><b>Model training</b></h3><h4><code>python train_GTN.py</code></h4><p dir="ltr">This step trains the GTN model. …”
-
100
Simulation Code and Raw Data
Published 2025“…<p dir="ltr">Reproducible code (Python) implementing a symmetrized split-step Fourier method (SSFM), with configuration files for all scans. …”