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Monarch: Expressive Structured Matrices for Efficient and Accurate Training.

, , , , , , , , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 4690-4721. PMLR, (2022)

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Scale-Equivariant Unrolled Neural Networks for Data-Efficient Accelerated MRI Reconstruction., , , , , , and . MICCAI (6), volume 13436 of Lecture Notes in Computer Science, page 737-747. Springer, (2022)The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset., , , , , , , , , and 19 other author(s). CoRR, (2020)Comp2Comp: Open-Source Body Composition Assessment on Computed Tomography., , , , , , , , , and 4 other author(s). CoRR, (2023)Prospector Heads: Generalized Feature Attribution for Large Models & Data., , , , , , , , and . ICML, OpenReview.net, (2024)SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation., , , , , , , , , and 2 other author(s). CoRR, (2022)Data-Limited Tissue Segmentation using Inpainting-Based Self-Supervised Learning., , , , , , , , , and 1 other author(s). CoRR, (2022)GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction., , , , , , , , and . CoRR, (2022)VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction., , , , , , , , and . MIDL, volume 172 of Proceedings of Machine Learning Research, page 325-352. PMLR, (2022)SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation., , , , , , , , , and 2 other author(s). NeurIPS Datasets and Benchmarks, (2021)Model ChangeLists: Characterizing Updates to ML Models., , , , , , and . FAccT, page 2432-2453. ACM, (2024)