Model Class Selection

The Dual Riemannian Newton Method on Statistical Manifolds presents a new optimization algorithm designed for probabilistic modeling. This method addresses the limitations of existing first-order techniques by utilizing a dual-connection structure, which enhances convergence rates near optimal solutions. The proposed algorithm is expected to improve efficiency in parameter estimation on statistical manifolds, leveraging the Fisher information metric and offering second-order updates.

The article discusses AfterLearnER (After Learning Evolutionary Retrofitting), a method that applies evolutionary optimization to enhance fully trained machine learning models. This process involves optimizing selected parameters or hyperparameters based on non-differentiable error signals from a subset of the validation set. The effectiveness of AfterLearnER is showcased through various applications, including depth sensing, speech re-synthesis, and image generation. This retrofitting can occur post-training or dynamically during inference, incorporating user feedback.