Muckley, Matthew J.

Results of the 2020 fastMRI challenge for machine learning MR image reconstruction

Muckley, M. J., Riemenschneider, B., Radmanesh, A., Kim, S., Jeong, G., Ko, J., Jun, Y., Shin, H., Hwang, D., Mostapha, M., et al. (2021) · IEEE Transactions on Medical Imaging

Advanced MRI Techniques and ApplicationsMedical Imaging Techniques and Applications

Cited by 313

Reducing Uncertainty in Undersampled MRI Reconstruction With Active Acquisition

Zhang, Z., Romero, A., Muckley, M. J., Vincent, P., Yang, L., & Drozdzal, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced MRI Techniques and ApplicationsImage and Signal Denoising Methods

Cited by 106

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning

Assran, M., Bardes, A., Fan, D., Garrido, Q., Howes, R., Mojtaba, Komeili, Muckley, M., Rizvi, A., Roberts, C., et al. (2025) · arXiv

Human Pose and Action RecognitionMultimodal Machine Learning Applications

Cited by 8

Training-free Linear Image Inverses via Flows

Pokle, A., Muckley, M. J., Chen, R. T. Q., & Karrer, B. (2023) · arXiv

Generative Adversarial Networks and Image SynthesisImage and Signal Denoising Methods

Cited by 0