IEEE

Application and Effectiveness Evaluation of Bayesian Optimization Algorithm in Hyperparameter Tuning of Machine Learning Models

Abstract: Hyperparameter tuning is a crucial step in the development of machine learning models, as it directly impacts their performance and generalization ability. Traditional methods for ...
GitHub

openai-fine-tuning-framework

A generic Python framework for fine-tuning OpenAI chat models on arbitrary tasks. Bring your own data, plug in a formatter and (optionally) a metric, and run the full pipeline — data preparation → ...
Scientific Research Publishing

Grossman, Z. and Paul, W.E. (2015) Dynamic Tuning of Lymphocytes: Physiological Basis, Mechanisms, and Function. Annual Review of Immunology, 33, 677-713.

ABSTRACT: The canonical affinity-threshold model of thymic T-cell selection posits that thymocyte fate is determined by TCR-pMHC integrated affinity. However, this model fails to explain high ...
marktechpost

A Coding Implementation to Build a Conditional Bayesian Hyperparameter Optimization Pipeline with Hyperopt, TPE, and Early Stopping

In this tutorial, we implement an advanced Bayesian hyperparameter optimization workflow using Hyperopt and the Tree-structured Parzen Estimator (TPE) algorithm. We construct a conditional search ...
IEEE

Reinforcement Learning-Based Hyperparameter Tuning for Adaptive Model Predictive Controllers in Battery Thermal Management

Abstract: Battery thermal management (BTM) is crucial for maximizing the battery performance and maintaining the battery health. Model Predictive Control (MPC) stands as a promising technique for ...