← Authors

Reinhard Heckel

24 papers · 2017–2025 · 9 conferences · across top CS/AI conferences

Achievements

🌈 Renaissance Researcher (8) πŸ—ΊοΈ Taxonomy Completionist (33) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (9) πŸŒ‰ Interdisciplinary Bridge 🧭 Keyword Pioneer 🌍 Conference Polyglot (9) πŸƒ Academic Marathon (8) πŸ† Keyword Champion (3) πŸ‘‘ Triple Crown πŸ‘₯ Mega-Team (60) πŸ—ƒοΈ Keyword Collector (69) ⚑ Prolific Year (6) πŸ’Ž Century Club (24) πŸ”₯ Unstoppable (9)

Conferences

ICML (7) NIPS (5) ICLR (4) AISTATS (3) CVPR (1) ECCV (1) JMLR (1) MIDL (1) WACV (1)

Top co-authors

Mahdi Soltanolkotabi (3) Tobit Klug (3) Mohammad Zalbagi Darestani (3) Igor Vasiljevic (2) Kannan Ramchandran (2) Yair Carmon (2) Jeffrey Li (2) Jenia Jitsev (2) Vaishaal Shankar (2) Jiayu Liu (2)

Keywords

image reconstruction (6) magnetic resonance imaging (4) image restoration (3) accelerated mri (3) self-supervised learning (2) image denoising (2) test-time training (2) compressive sensing (2) ridge regularization (2) inverse problem (2) distribution shift (2) sample complexity (2) domain adaptation (2) deep learning (2) mri reconstruction (2) data augmentation (1) medical imaging (1) adversarial robustness (1) motion estimation (1) vision transformer (1)

Papers

Language models scale reliably with over-training and on downstream tasks ICLR 2025
Efficient Noise Calculation in Deep Learning-based MRI Reconstructions ICML 2025
Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data ICML 2024
DataComp-LM: In search of the next generation of training sets for language models NIPS 2024
IR-FRestormer: Iterative Refinement With Fourier-Based Restormer for Accelerated MRI Reconstruction WACV 2024
Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization JMLR 2024
MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI NIPS 2024
TTT-MIM: Test-Time Training with Masked Image Modeling for Denoising Distribution Shifts ECCV 2024
Scaling Laws For Deep Learning Based Image Reconstruction ICLR 2023
Learning Provably Robust Estimators for Inverse Problems via Jittering NIPS 2023
Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods NIPS 2023
Zero-Shot Noise2Noise: Efficient Image Denoising Without Any Data CVPR 2023
Vision Transformers Enable Fast and Robust Accelerated MRI MIDL 2022
Provable Continual Learning via Sketched Jacobian Approximations AISTATS 2022
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing ICML 2022
Data augmentation for deep learning based accelerated MRI reconstruction with limited data ICML 2021
Interpolation can hurt robust generalization even when there is no noise NIPS 2021
Early Stopping in Deep Networks: Double Descent and How to Eliminate it ICLR 2021
Measuring Robustness in Deep Learning Based Compressive Sensing ICML 2021
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation ICML 2020
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks ICLR 2019
Adaptive Estimation for Approximate $k$-Nearest-Neighbor Computations AISTATS 2019
Approximate Ranking from Pairwise Comparisons AISTATS 2018
The Sample Complexity of Online One-Class Collaborative Filtering ICML 2017