Search Results for "model-builder"

Uncertainty Baselines is a collection of strong, well-documented training pipelines that make it straightforward to evaluate predictive uncertainty in modern machine learning models. Rather than offering toy scripts, it provides end-to-end recipes—data input, model architectures, training loops, evaluation metrics, and logging—so results are comparable across runs and research groups. The library spans canonical modalities and tasks, from image classification and NLP to tabular problems, with baselines that cover both deterministic and probabilistic approaches. Techniques include deep ensembles, Monte Carlo dropout, temperature scaling, stochastic variational inference, heteroscedastic heads, and out-of-distribution detection workflows. ...

This is a template Python code set which makes it easy to use JModelica to solve optimal control problem. The template includes a sample model definition file (opt_definition.mop) and a .bat file (run_me.bat) to start its calculation. After download the template, immediately you can run JModelica by only double-clicking run_me.bat file, and obtain the optimization result. Please go to the page given below for the information of how to start to use this and its details.