Loss Landscape

Loss landscapes, which represent the relationship between a neural network's parameters and its loss function, are a central focus in understanding deep learning's success and limitations. Current research investigates the geometry of these landscapes, particularly focusing on the presence of linear paths connecting different solutions (mode connectivity) and the impact of landscape flatness on generalization and catastrophic forgetting, across various architectures including vision transformers and recurrent networks. Understanding loss landscape properties is crucial for improving training algorithms, developing more robust models, and gaining deeper insights into the inner workings of neural networks, ultimately leading to more efficient and effective machine learning systems.