Model Optimization

Model optimization focuses on improving the efficiency, accuracy, and resource utilization of machine learning models. Current research emphasizes techniques like reinforcement learning, knowledge distillation, and hyperparameter optimization across diverse model architectures, including deep neural networks and gradient boosting algorithms, to achieve these goals. This work is crucial for deploying AI in resource-constrained environments (e.g., embedded systems, wearable devices) and for addressing challenges like imbalanced datasets and model confidentiality, ultimately advancing both the theoretical understanding and practical applications of machine learning.