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Matthew J. Muckley
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2020 – today
- 2024
- [j4]Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer:
Training-free linear image inverses via flows. Trans. Mach. Learn. Res. 2024 (2024) - [c9]Marlène Careil, Matthew J. Muckley, Jakob Verbeek, Stéphane Lathuilière:
Towards image compression with perfect realism at ultra-low bitrates. ICLR 2024 - [c8]Iris A. M. Huijben, Matthijs Douze, Matthew J. Muckley, Ruud van Sloun, Jakob Verbeek:
Residual Quantization with Implicit Neural Codebooks. ICML 2024 - [i14]Iris A. M. Huijben, Matthijs Douze, Matthew J. Muckley, Ruud van Sloun, Jakob Verbeek:
Residual Quantization with Implicit Neural Codebooks. CoRR abs/2401.14732 (2024) - [i13]Pietro Astolfi, Marlène Careil, Melissa Hall, Oscar Mañas, Matthew J. Muckley, Jakob Verbeek, Adriana Romero-Soriano, Michal Drozdzal:
Consistency-diversity-realism Pareto fronts of conditional image generative models. CoRR abs/2406.10429 (2024) - [i12]Buu Phan, Marton Havasi, Matthew J. Muckley, Karen Ullrich:
Understanding and Mitigating Tokenization Bias in Language Models. CoRR abs/2406.16829 (2024) - 2023
- [j3]Alaaeldin El-Nouby, Matthew J. Muckley, Karen Ullrich, Ivan Laptev, Jakob Verbeek, Hervé Jégou:
Image Compression with Product Quantized Masked Image Modeling. Trans. Mach. Learn. Res. 2023 (2023) - [c7]Matthew J. Muckley, Alaaeldin El-Nouby, Karen Ullrich, Hervé Jégou, Jakob Verbeek:
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models. ICML 2023: 25426-25443 - [i11]Matthew J. Muckley, Alaaeldin El-Nouby, Karen Ullrich, Hervé Jégou, Jakob Verbeek:
Improving Statistical Fidelity for Neural Image Compression with Implicit Local Likelihood Models. CoRR abs/2301.11189 (2023) - [i10]Ashwini Pokle, Matthew J. Muckley, Ricky T. Q. Chen, Brian Karrer:
Training-free Linear Image Inversion via Flows. CoRR abs/2310.04432 (2023) - [i9]Marlène Careil, Matthew J. Muckley, Jakob Verbeek, Stéphane Lathuilière:
Towards image compression with perfect realism at ultra-low bitrates. CoRR abs/2310.10325 (2023) - 2022
- [c6]Tim Bakker, Matthew J. Muckley, Adriana Romero-Soriano, Michal Drozdzal, Luis Pineda:
On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction. MIDL 2022: 63-85 - [i8]Tim Bakker, Matthew J. Muckley, Adriana Romero-Soriano, Michal Drozdzal, Luis Pineda:
On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction. CoRR abs/2203.16392 (2022) - [i7]Alaaeldin El-Nouby, Matthew J. Muckley, Karen Ullrich, Ivan Laptev, Jakob Verbeek, Hervé Jégou:
Image Compression with Product Quantized Masked Image Modeling. CoRR abs/2212.07372 (2022) - [i6]Ricky T. Q. Chen, Matthew Le, Matthew J. Muckley, Maximilian Nickel
, Karen Ullrich:
Latent Discretization for Continuous-time Sequence Compression. CoRR abs/2212.13659 (2022) - 2021
- [j2]Matthew J. Muckley
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE Trans. Medical Imaging 40(9): 2306-2317 (2021) - [c5]Patricia M. Johnson, Geunu Jeong, Kerstin Hammernik, Jo Schlemper, Chen Qin
Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge. MLMIR@MICCAI 2021: 25-34 - [i5]Anuroop Sriram, Matthew J. Muckley, Koustuv Sinha, Farah Shamout, Joelle Pineau, Krzysztof J. Geras, Lea Azour, Yindalon Aphinyanaphongs, Nafissa Yakubova, William Moore:
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction. CoRR abs/2101.04909 (2021) - 2020
- [i4]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:
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge. CoRR abs/2001.02518 (2020) - [i3]Matthew J. Muckley, Bruno Riemenschneider
State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge. CoRR abs/2012.06318 (2020)
2010 – 2019
- 2019
- [c4]Zizhao Zhang, Adriana Romero, Matthew J. Muckley
Reducing Uncertainty in Undersampled MRI Reconstruction With Active Acquisition. CVPR 2019: 2049-2058 - [c3]Patricia M. Johnson, Matthew J. Muckley
Joint Multi-anatomy Training of a Variational Network for Reconstruction of Accelerated Magnetic Resonance Image Acquisitions. MLMIR@MICCAI 2019: 71-79 - [i2]Zizhao Zhang, Adriana Romero, Matthew J. Muckley, Pascal Vincent, Lin Yang, Michal Drozdzal:
Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition. CoRR abs/1902.03051 (2019) - 2018
- [c2]Erich Kobler, Matthew J. Muckley
Variational Deep Learning for Low-Dose Computed Tomography. ICASSP 2018: 6687-6691 - [i1]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, Adriana Romero, Michael G. Rabbat, Pascal Vincent, James Pinkerton, Duo Wang, Nafissa Yakubova, Erich Owens, C. Lawrence Zitnick, Michael P. Recht, Daniel K. Sodickson, Yvonne W. Lui:
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI. CoRR abs/1811.08839 (2018) - 2015
- [j1]Matthew J. Muckley
Fast Parallel MR Image Reconstruction via B1-Based, Adaptive Restart, Iterative Soft Thresholding Algorithms (BARISTA). IEEE Trans. Medical Imaging 34(2): 578-588 (2015) - 2014
- [c1]Matthew J. Muckley
Fast MR image reconstruction with orthogonal wavelet regularization via shift-variant shrinkage. ICIP 2014: 3651-3655
Coauthor Index
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last updated on 2024-10-28 21:18 CET by the dblp team
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