Parameter Prediction for Unseen Deep Architectures - With First Author Boris Knyazev

This course focuses on predicting final network parameters for unseen deep architectures without training them. The learning outcomes include understanding Graph-Hypernetworks, training on diverse DNN-architectures, and predicting high-performing weights. The course teaches skills such as utilizing Graph Neural Networks, message passing, differentiable normalization, and meta-batching. The teaching method involves a lecture-style format with explanations and experimental results. The intended audience for this course includes deep learning enthusiasts, researchers, and practitioners interested in neural architecture search and meta-learning.