No articles match
Hierarchical Classification13 days ago
Data format | Data preparation | Node preparation rules for {tabnet} models | Avoid factor predictors | Avoid column name collision with reserved {data.tree} names | Avoid column named level_* to avoid collision with output data.tree names | Ensure the last hierarchy of the tree is the observation id | Ensure there is a root level in the hierarchy | Ensure there is no missing values in the hierarchical classes | Model building | Data set split | Model diagnostic | Model inference
Self-supervised training and fine-tuning13 days ago
Data preprocessing | Self-supervised training step | Continuing training with supervised task | Comparing against a model without pretraining
RF100 Dataset Catalog2 months ago
Overview | Quick Search | Example: Finding a Photovoltaic Dataset | Complete Catalog | Collections | Biology Collection (9 datasets) | Medical Collection (8 datasets) | Infrared Collection (4 datasets) | Damage Collection (3 datasets) | Underwater Collection (4 datasets) | Document Collection (6 datasets) | Usage Example | Dataset Statistics | Filtering and Exploration | Additional Resources | Citation
Installation3 months ago
Windows | CPU | GPU | MacOS | Linux | Installing from pre-built binaries | Using cudatoolkit R packages | Environment variables | Troubleshooting | Debug installation messages | Large file download timeout | File based download | Installing older versions
Modify prompt enhancements8 months ago
Support for glue
Other interfaces8 months ago
Output | Using the chattr() function | Highlight the request in a script
Interpretation tools10 months ago
Experiments | Datasets | Syn2 | Syn 4
Using ROC AUM loss for imbalanced binary classification10 months ago
Introduction | How imbalance is my problem ? | Solutions to improve imbalanced classification models | Using the AUC metric and pr_curve() plots | Case-weight | ROC_AUM loss | All together
Fitting tabnet with tidymodels1 years ago
Training a Tabnet model from missing-values dataset1 years ago
Motivation | Missing-data dataset creation | Ames missings understanding | While keeping some room for freedom | Ames with missing data | Model pretraining | Variable importance with raw ames dataset | Variable importance with ames_missing dataset | Model training | Conclusion
Custom loops with luz3 years ago
Multiple optimizers | Fully flexible step | Next steps
Get started with luz3 years ago
Training a nn_module | The training loop | Metrics | Evaluate | Customizing with callbacks | Next steps
Accelerator API4 years ago
Example