Maximum Likelihood Training

Maximum likelihood training (MLT) aims to find model parameters that maximize the likelihood of observed data, a fundamental approach in statistical modeling and machine learning. Current research focuses on improving MLT's efficiency and effectiveness across diverse model architectures, including normalizing flows, energy-based models, and diffusion models, often addressing limitations like computational cost and sensitivity to outliers. These advancements are impacting various fields, from improving generative models for image synthesis and outlier detection in critical systems to enhancing the accuracy and reliability of probabilistic predictions in deep learning. Addressing challenges such as the computational complexity of MLT for certain models, like determinantal point processes, remains an active area of investigation.