Explore advanced techniques for MR reconstruction in this 47-minute Stanford University lecture by PhD student Beliz Gunel. Dive into the use of untrained neural networks as image priors for solving inverse problems, and discover how ConvDecoder can generate weakly-labeled data from undersampled MR scans. Learn about a novel approach combining few supervised pairs with weakly supervised pairs to train an unrolled neural network, achieving strong reconstruction performance with fast inference time. Compare this method to supervised and self-training baselines in limited data scenarios, and gain insights into applying self-training in natural language understanding. Examine key observations, current challenges, and ongoing work in MR reconstruction, including motion simulation and the importance of standardizing ML pipelines.