Heckel, Reinhard

28 publications

ICML 2025 Efficient Noise Calculation in Deep Learning-Based MRI Reconstructions Onat Dalmaz, Arjun D Desai, Reinhard Heckel, Tolga Cukur, Akshay S Chaudhari, Brian Hargreaves

NeurIPS 2025 Improving Deep Learning for Accelerated MRI with Data Filtering Kang Lin, Anselm Krainovic, Kun Wang, Reinhard Heckel

ICLR 2025 Language Models Scale Reliably with Over-Training and on Downstream Tasks Samir Yitzhak Gadre, Georgios Smyrnis, Vaishaal Shankar, Suchin Gururangan, Mitchell Wortsman, Rulin Shao, Jean Mercat, Alex Fang, Jeffrey Li, Sedrick Keh, Rui Xin, Marianna Nezhurina, Igor Vasiljevic, Luca Soldaini, Jenia Jitsev, Alex Dimakis, Gabriel Ilharco, Pang Wei Koh, Shuran Song, Thomas Kollar, Yair Carmon, Achal Dave, Reinhard Heckel, Niklas Muennighoff, Ludwig Schmidt

NeurIPS 2025 Measuring Fingerprints of Web-Filtered Text Datasets and Fingerprint Propagation Through Training Youssef Mansour, Reinhard Heckel

NeurIPS 2024 DataComp-LM: In Search of the Next Generation of Training Sets for Language Models Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Gadre, Hritik Bansal, Etash Guha, Sedrick Keh, Kushal Arora, Saurabh Garg, Rui Xin, Niklas Muennighoff, Reinhard Heckel, Jean Mercat, Mayee Chen, Suchin Gururangan, Mitchell Wortsman, Alon Albalak, Yonatan Bitton, Marianna Nezhurina, Amro Abbas, Cheng-Yu Hsieh, Dhruba Ghosh, Josh Gardner, Maciej Kilian, Hanlin Zhang, Rulin Shao, Sarah Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Chandu, Thao Nguyen, Igor Vasiljevic, Sham Kakade, Shuran Song, Sujay Sanghavi, Fartash Faghri, Sewoong Oh, Luke Zettlemoyer, Kyle Lo, Alaaeldin El-Nouby, Hadi Pouransari, Alexander Toshev, Stephanie Wang, Dirk Groeneveld, Luca Soldaini, Pang Wei Koh, Jenia Jitsev, Thomas Kollar, Alexandros G. Dimakis, Yair Carmon, Achal Dave, Ludwig Schmidt, Vaishaal Shankar

WACV 2024 IR-FRestormer: Iterative Refinement with Fourier-Based Restormer for Accelerated MRI Reconstruction Mohammad Zalbagi Darestani, Vishwesh Nath, Wenqi Li, Yufan He, Holger R. Roth, Ziyue Xu, Daguang Xu, Reinhard Heckel, Can Zhao

JMLR 2024 Monotonic Risk Relationships Under Distribution Shifts for Regularized Risk Minimization Daniel LeJeune, Jiayu Liu, Reinhard Heckel

NeurIPS 2024 MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI Tobit Klug, Kun Wang, Stefan Ruschke, Reinhard Heckel

ICML 2024 Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data Kang Lin, Reinhard Heckel

ECCV 2024 TTT-MIM: Test-Time Training with Masked Image Modeling for Denoising Distribution Shifts Youssef Mansour, Xuyang Zhong, Serdar Caglar, Reinhard Heckel

NeurIPS 2023 Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods Tobit Klug, Dogukan Atik, Reinhard Heckel

NeurIPS 2023 Learning Provably Robust Estimators for Inverse Problems via Jittering Anselm Krainovic, Mahdi Soltanolkotabi, Reinhard Heckel

ICLR 2023 Scaling Laws for Deep Learning Based Image Reconstruction Tobit Klug, Reinhard Heckel

MIDL 2023 Vision Transformers Enable Fast and Robust Accelerated MRI Kang Lin, Reinhard Heckel

CVPR 2023 Zero-Shot Noise2Noise: Efficient Image Denoising Without Any Data Youssef Mansour, Reinhard Heckel

AISTATS 2022 Provable Continual Learning via Sketched Jacobian Approximations Reinhard Heckel

ICML 2022 Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing Mohammad Zalbagi Darestani, Jiayu Liu, Reinhard Heckel

ICML 2021 Data Augmentation for Deep Learning Based Accelerated MRI Reconstruction with Limited Data Zalan Fabian, Reinhard Heckel, Mahdi Soltanolkotabi

ICLR 2021 Early Stopping in Deep Networks: Double Descent and How to Eliminate It Reinhard Heckel, Fatih Furkan Yilmaz

NeurIPS 2021 Interpolation Can Hurt Robust Generalization Even When There Is No Noise Konstantin Donhauser, Alexandru Tifrea, Michael Aerni, Reinhard Heckel, Fanny Yang

ICMLW 2021 Maximizing the Robust Margin Provably Overfits on Noiseless Data Konstantin Donhauser, Alexandru Tifrea, Michael Aerni, Reinhard Heckel, Fanny Yang

ICML 2021 Measuring Robustness in Deep Learning Based Compressive Sensing Mohammad Zalbagi Darestani, Akshay S Chaudhari, Reinhard Heckel

ICML 2020 Compressive Sensing with Un-Trained Neural Networks: Gradient Descent Finds a Smooth Approximation Reinhard Heckel, Mahdi Soltanolkotabi

ICLR 2020 Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators Reinhard Heckel, Mahdi Soltanolkotabi

AISTATS 2019 Adaptive Estimation for Approximate $k$-Nearest-Neighbor Computations Daniel LeJeune, Reinhard Heckel, Richard Baraniuk

ICLR 2019 Deep Decoder: Concise Image Representations from Untrained Non-Convolutional Networks Reinhard Heckel, Paul Hand

AISTATS 2018 Approximate Ranking from Pairwise Comparisons Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright

ICML 2017 The Sample Complexity of Online One-Class Collaborative Filtering Reinhard Heckel, Kannan Ramchandran