Abstract: Large pre-trained models trained on internet-scale data are often not ready for safe deployment out-of-the-box. They are heavily fine-tuned and aligned using large quantities of human preference data, usually […]

Abstract: Foundation models are monolithic models that are trained on a broad set of data, and which are then in principle fine-tuned to various specific tasks. But they are ill-suited […]

Abstract: The explicit incorporation of task-specific inductive biases through symmetry has emerged as a crucial design precept in the development of high-performance machine learning models. Symmetry-aware neural networks, such as […]