Loss Surface

Loss surfaces, representing the landscape of a machine learning model's loss function across its parameter space, are a central focus in understanding model training and generalization. Current research investigates the geometry of these surfaces, particularly focusing on the impact of factors like sample size, Hessian properties, and the relationship between loss flatness and representation compression, often using stochastic gradient descent (SGD) and employing techniques like adversarial training and knowledge distillation. These studies aim to improve model robustness, uncertainty quantification, and generalization performance, with implications for diverse applications ranging from atomistic force fields to high-energy physics. Ultimately, a deeper understanding of loss surfaces promises to enhance the design and training of more effective and reliable machine learning models.