Fast MR image reconstruction with orthogonal wavelet regularization via shift-variant shrinkage. - Matthew J. Muckley; Jeffrey A. Fessler (ICIP, 2014)

Fast Parallel MR Image Reconstruction via B1-Based, Adaptive Restart, Iterative Soft Thresholding Algorithms (BARISTA). - Matthew J. Muckley; Douglas C. Noll; Jeffrey A. Fessler (IEEE Trans. Medical Imaging, 2015)

Variational Deep Learning for Low-Dose Computed Tomography. - Erich Kobler; Matthew J. Muckley; Baiyu Chen; Florian Knoll; Kerstin Hammernik; Thomas Pock; Daniel K. Sodickson; Ricardo Otazo (ICASSP, 2018)

fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. - Jure Zbontar; Florian Knoll; Anuroop Sriram; Matthew J. Muckley; Mary Bruno; Aaron Defazio; Marc Parente; Krzysztof J. Geras; Joe Katsnelson; Hersh Chandarana; Zizhao Zhang; Michal Drozdzal; et al. (CoRR, 2018)

Reducing Uncertainty in Undersampled MRI Reconstruction With Active Acquisition. - Zizhao Zhang; Adriana Romero; Matthew J. Muckley; Pascal Vincent; Lin Yang 002; Michal Drozdzal (CVPR, 2019)

Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. - Patricia M. Johnson; Matthew J. Muckley; Mary Bruno; Erich Kobler; Kerstin Hammernik; Thomas Pock; Florian Knoll (MLMIR@MICCAI, 2019)

Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. - Florian Knoll; Tullie Murrell; Anuroop Sriram; Nafissa Yakubova; Jure Zbontar; Michael G. Rabbat; Aaron Defazio; Matthew J. Muckley; Daniel K. Sodickson; C. Lawrence Zitnick; Michael P. Recht (CoRR, 2020)

State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge. - Matthew J. Muckley; Bruno Riemenschneider; Alireza Radmanesh; Sunwoo Kim; Geunu Jeong; Jingyu Ko; Yohan Jun; Hyungseob Shin; Dosik Hwang; Mahmoud Mostapha; Simon Arberet; Dominik Nickel; et al. (CoRR, 2020)

Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge. - Patricia M. Johnson; Geunu Jeong; Kerstin Hammernik; Jo Schlemper; Chen Qin; Jinming Duan 001; Daniel Rueckert; Jingu Lee; Nicola Pezzotti; Elwin de Weerdt; Sahar Yousefi; Mohamed S. Elmahdy; et al. (MLMIR@MICCAI, 2021)

COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction. - Anuroop Sriram; Matthew J. Muckley; Koustuv Sinha; Farah Shamout; Joelle Pineau; Krzysztof J. Geras; Lea Azour; Yindalon Aphinyanaphongs; Nafissa Yakubova; William Moore (CoRR, 2021)

Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. - Matthew J. Muckley; Bruno Riemenschneider; Alireza Radmanesh; Sunwoo Kim; Geunu Jeong; Jingyu Ko; Yohan Jun; Hyungseob Shin; Dosik Hwang; Mahmoud Mostapha; Simon Arberet; Dominik Nickel; et al. (IEEE Trans. Medical Imaging, 2021)

On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction. - Tim Bakker; Matthew J. Muckley; Adriana Romero-Soriano; Michal Drozdzal; Luis Pineda (MIDL, 2022)

Latent Discretization for Continuous-time Sequence Compression. - Ricky T. Q. Chen; Matthew Le 001; Matthew J. Muckley; Maximilian Nickel; Karen Ullrich (CoRR, 2022)

Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models. - Matthew J. Muckley; Alaaeldin El-Nouby; Karen Ullrich; Hervé Jégou; Jakob Verbeek (ICML, 2023)

Training-free Linear Image Inversion via Flows. - Ashwini Pokle; Matthew J. Muckley; Ricky T. Q. Chen; Brian Karrer (CoRR, 2023)

Image Compression with Product Quantized Masked Image Modeling. - Alaaeldin El-Nouby; Matthew J. Muckley; Karen Ullrich; Ivan Laptev; Jakob Verbeek; Hervé Jégou (Trans. Mach. Learn. Res., 2023)

Towards image compression with perfect realism at ultra-low bitrates. - Marlène Careil; Matthew J. Muckley; Jakob Verbeek; Stéphane Lathuilière (ICLR, 2024)

Residual Quantization with Implicit Neural Codebooks. - Iris A. M. Huijben; Matthijs Douze; Matthew J. Muckley; Ruud van Sloun; Jakob Verbeek (ICML, 2024)

On improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models. - Tariq Berrada Ifriqi; Pietro Astolfi; Melissa Hall; Reyhane Askari Hemmat; Yohann Benchetrit; Marton Havasi; Matthew J. Muckley; Karteek Alahari; Adriana Romero-Soriano; Jakob Verbeek; Michal Drozdzal (NeurIPS, 2024)

Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning. - Théo Moutakanni; Piotr Bojanowski; Guillaume Chassagnon; Céline Hudelot; Armand Joulin; Yann LeCun; Matthew J. Muckley; Maxime Oquab; Marie-Pierre Revel; Maria Vakalopoulou (CoRR, 2024)

Consistency-diversity-realism Pareto fronts of conditional image generative models. - Pietro Astolfi; Marlène Careil; Melissa Hall; Oscar Mañas; Matthew J. Muckley; Jakob Verbeek; Adriana Romero-Soriano; Michal Drozdzal (CoRR, 2024)

Understanding and Mitigating Tokenization Bias in Language Models. - Buu Phan; Marton Havasi; Matthew J. Muckley; Karen Ullrich (CoRR, 2024)

On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models. - Tariq Berrada; Pietro Astolfi; Melissa Hall; Reyhane Askari Hemmat; Yohann Benchetrit; Marton Havasi; Matthew J. Muckley; Karteek Alahari; Adriana Romero-Soriano; Jakob Verbeek; Michal Drozdzal (CoRR, 2024)

Training-free linear image inverses via flows. - Ashwini Pokle; Matthew J. Muckley; Ricky T. Q. Chen; Brian Karrer (Trans. Mach. Learn. Res., 2024)

Enhancing and Evaluating Probabilistic Circuits for High-Resolution Lossless Image Compression. - Daniel Severo 001; Jingtong Su; Anji Liu; Jeff Johnson; Brian Karrer; Guy Van den Broeck; Matthew J. Muckley; Karen Ullrich (DCC, 2025)

Architecture Optimizations for Improving Neural Image Compression Compute Complexity. - Matthew J. Muckley; Marton Havasi; Jakob Verbeek; Karen Ullrich (DCC, 2025)

Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles. - Buu Phan; Brandon Amos; Itai Gat; Marton Havasi; Matthew J. Muckley; Karen Ullrich (ICLR, 2025)

Qinco2: Vector Compression and Search with Improved Implicit Neural Codebooks. - Théophane Vallaeys; Matthew J. Muckley; Jakob Verbeek; Matthijs Douze (ICLR, 2025)

Lossless Compression of Vector IDs for Approximate Nearest Neighbor Search. - Daniel Severo 001; Giuseppe Ottaviano; Matthew J. Muckley; Karen Ullrich; Matthijs Douze (CoRR, 2025)

DIMCIM: A Quantitative Evaluation Framework for Default-mode Diversity and Generalization in Text-to-Image Generative Models. - Revant Teotia; Candace Ross; Karen Ullrich; Sumit Chopra; Adriana Romero-Soriano; Melissa Hall; Matthew J. Muckley (CoRR, 2025)

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning. - Mido Assran; Adrien Bardes; David Fan; Quentin Garrido; Russell Howes; Mojtaba Komeili; Matthew J. Muckley; Ammar Rizvi; Claire Roberts; Koustuv Sinha; Artem Zholus; Sergio Arnaud; et al. (CoRR, 2025)