If you use our papers or software in your work, please consider citing the corresponding publication (more to be added).
arXiv2025
No alignment needed for generation: Learning linearly separable representations in diffusion models
arXiv2025
No alignment needed for generation: Learning linearly separable representations in diffusion models
NeurIPS2025
Time-embedded algorithm unrolling for computational MRI
NeurIPS2025
Time-embedded algorithm unrolling for computational MRI
NeurIPS2025
Time-embedded algorithm unrolling for computational MRI
NeurIPS (Spotlight⭐)2025
Fast MRI for all: Bridging access gaps by training without raw data
NeurIPS (Spotlight⭐)2025
Fast MRI for all: Bridging access gaps by training without raw data
NeurIPS (Spotlight⭐)2025
Fast MRI for all: Bridging access gaps by training without raw data
MRM (Magnetic Resonance in Medicine)2020
Self-supervised learning of physics-guided reconstruction neural networks without fully-sampled reference data
MRM (Magnetic Resonance in Medicine)2020
Self-supervised learning of physics-guided reconstruction neural networks without fully-sampled reference data
MRM (Magnetic Resonance in Medicine)2020
Self-supervised learning of physics-guided reconstruction neural networks without fully-sampled reference data
MRM (Magnetic Resonance in Medicine)2020
Self-supervised learning of physics-guided reconstruction neural networks without fully-sampled reference data
MRM (Magnetic Resonance in Medicine)2020
Self-supervised learning of physics-guided reconstruction neural networks without fully-sampled reference data
MRM (Magnetic Resonance in Medicine)2020