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Reinhard Heckel
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2020 – today
- 2024
- [j15]Daniel LeJeune, Jiayu Liu, Reinhard Heckel:
Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization. J. Mach. Learn. Res. 25: 54:1-54:37 (2024) - [j14]Johannes F. Kunz
, Stefan Ruschke
Implicit Neural Networks With Fourier-Feature Inputs for Free-Breathing Cardiac MRI Reconstruction. IEEE Trans. Computational Imaging 10: 1280-1289 (2024) - [c37]Kang Lin, Reinhard Heckel:
Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data. ICML 2024 - [c36]Mohammad Zalbagi Darestani, Vishwesh Nath, Wenqi Li, Yufan He, Holger R. Roth, Ziyue Xu, Daguang Xu, Reinhard Heckel, Can Zhao:
IR-FRestormer: Iterative Refinement with Fourier-Based Restormer for Accelerated MRI Reconstruction. WACV 2024: 7640-7649 - [i62]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, Jenia Jitsev, Alexandros G. Dimakis, Gabriel Ilharco, Shuran Song, Thomas Kollar, Yair Carmon, Achal Dave, Reinhard Heckel, Niklas Muennighoff, Ludwig Schmidt:
Language models scale reliably with over-training and on downstream tasks. CoRR abs/2403.08540 (2024) - [i61]Youssef Mansour, Reinhard Heckel:
GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration. CoRR abs/2404.00807 (2024) - [i60]Reinhard Heckel, Mathews Jacob, Akshay Chaudhari, Or Perlman, Efrat Shimron:
Deep Learning for Accelerated and Robust MRI Reconstruction: a Review. CoRR abs/2404.15692 (2024) - [i59]Jeffrey Li, Alex Fang, Georgios Smyrnis, Maor Ivgi, Matt Jordan, Samir Yitzhak Gadre, Hritik Bansal, Etash Kumar 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 M. Pratt, Sunny Sanyal, Gabriel Ilharco, Giannis Daras, Kalyani Marathe, Aaron Gokaslan, Jieyu Zhang, Khyathi Raghavi Chandu, Thao Nguyen, Igor Vasiljevic, Sham M. 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:
DataComp-LM: In search of the next generation of training sets for language models. CoRR abs/2406.11794 (2024) - [i58]Tobit Klug, Kun Wang, Stefan Ruschke, Reinhard Heckel:
MotionTTT: 2D Test-Time-Training Motion Estimation for 3D Motion Corrected MRI. CoRR abs/2409.09370 (2024) - 2023
- [c35]Franziska Weindel, Andreas L. Gimpel, Robert N. Grass, Reinhard Heckel:
Embracing errors is more effective than avoiding them through constrained coding for DNA data storage. Allerton 2023: 1-8 - [c34]Youssef Mansour, Reinhard Heckel:
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data. CVPR 2023: 14018-14027 - [c33]Tobit Klug, Reinhard Heckel:
Scaling Laws For Deep Learning Based Image Reconstruction. ICLR 2023 - [c32]Tobit Klug, Dogukan Atik, Reinhard Heckel:
Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods. NeurIPS 2023 - [c31]Anselm Krainovic, Mahdi Soltanolkotabi, Reinhard Heckel:
Learning Provably Robust Estimators for Inverse Problems via Jittering. NeurIPS 2023 - [d1]Johannes Künzel, Stefan Ruschke, Reinhard Heckel:
Free-breathing 2D Cartesian Cardiac MRI Datasets. IEEE DataPort, 2023 - [i57]Youssef Mansour, Reinhard Heckel:
Zero-Shot Noise2Noise: Efficient Image Denoising without any Data. CoRR abs/2303.11253 (2023) - [i56]Johannes F. Kunz, Stefan Ruschke, Reinhard Heckel:
Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction. CoRR abs/2305.06822 (2023) - [i55]Tobit Klug, Dogukan Atik, Reinhard Heckel:
Analyzing the Sample Complexity of Self-Supervised Image Reconstruction Methods. CoRR abs/2305.19079 (2023) - [i54]Anselm Krainovic, Mahdi Soltanolkotabi
Learning Provably Robust Estimators for Inverse Problems via Jittering. CoRR abs/2307.12822 (2023) - [i53]Stefan Bamberger, Reinhard Heckel, Felix Krahmer:
Approximating Positive Homogeneous Functions with Scale Invariant Neural Networks. CoRR abs/2308.02836 (2023) - [i52]Frédéric Wang, Han Qi, Alfredo De Goyeneche, Reinhard Heckel, Michael Lustig, Efrat Shimron:
K-band: Self-supervised MRI Reconstruction via Stochastic Gradient Descent over K-space Subsets. CoRR abs/2308.02958 (2023) - [i51]Franziska Weindel, Andreas L. Gimpel, Robert N. Grass, Reinhard Heckel:
Embracing Errors is More Efficient than Avoiding Them through Constrained Coding for DNA Data Storage. CoRR abs/2308.05952 (2023) - [i50]Simon Wiedemann, Reinhard Heckel:
A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron Tomography. CoRR abs/2311.05539 (2023) - [i49]Kang Lin, Reinhard Heckel:
Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data. CoRR abs/2312.10271 (2023) - 2022
- [j13]Ilan Shomorony, Reinhard Heckel:
Information-Theoretic Foundations of DNA Data Storage. Found. Trends Commun. Inf. Theory 19(1): 1-106 (2022) - [j12]Paul Hand, Reinhard Heckel, Jonathan Scarlett
Guest Editorial. IEEE J. Sel. Areas Inf. Theory 3(3): 432 (2022) - [j11]Jonathan Scarlett
Theoretical Perspectives on Deep Learning Methods in Inverse Problems. IEEE J. Sel. Areas Inf. Theory 3(3): 433-453 (2022) - [j10]Samuel Rey
Untrained Graph Neural Networks for Denoising. IEEE Trans. Signal Process. 70: 5708-5723 (2022) - [c30]Reinhard Heckel:
Provable Continual Learning via Sketched Jacobian Approximations. AISTATS 2022: 10448-10470 - [c29]Kel Levick, Reinhard Heckel, Ilan Shomorony:
Achieving the Capacity of a DNA Storage Channel with Linear Coding Schemes. CISS 2022: 218-223 - [c28]Mohammad Zalbagi Darestani, Jiayu Liu, Reinhard Heckel:
Test-Time Training Can Close the Natural Distribution Shift Performance Gap in Deep Learning Based Compressed Sensing. ICML 2022: 4754-4776 - [c27]Fatih Furkan Yilmaz, Reinhard Heckel:
Regularization-wise double descent: Why it occurs and how to eliminate it. ISIT 2022: 426-431 - [c26]Kang Lin, Reinhard Heckel:
Vision Transformers Enable Fast and Robust Accelerated MRI. MIDL 2022: 774-795 - [i48]Youssef Mansour, Kang Lin, Reinhard Heckel:
Image-to-Image MLP-mixer for Image Reconstruction. CoRR abs/2202.02018 (2022) - [i47]Fatih Furkan Yilmaz, Reinhard Heckel:
Regularization-wise double descent: Why it occurs and how to eliminate it. CoRR abs/2206.01378 (2022) - [i46]Jonathan Scarlett, Reinhard Heckel, Miguel R. D. Rodrigues, Paul Hand, Yonina C. Eldar:
Theoretical Perspectives on Deep Learning Methods in Inverse Problems. CoRR abs/2206.14373 (2022) - [i45]Tobit Klug, Reinhard Heckel:
Scaling Laws For Deep Learning Based Image Reconstruction. CoRR abs/2209.13435 (2022) - [i44]Fatih Furkan Yilmaz, Reinhard Heckel:
Test-time Recalibration of Conformal Predictors Under Distribution Shift Based on Unlabeled Examples. CoRR abs/2210.04166 (2022) - [i43]Daniel LeJeune, Jiayu Liu, Reinhard Heckel:
Monotonic Risk Relationships under Distribution Shifts for Regularized Risk Minimization. CoRR abs/2210.11589 (2022) - [i42]Ilan Shomorony, Reinhard Heckel:
Information-Theoretic Foundations of DNA Data Storage. CoRR abs/2211.05552 (2022) - 2021
- [j9]Mohammad Zalbagi Darestani
Accelerated MRI With Un-Trained Neural Networks. IEEE Trans. Computational Imaging 7: 724-733 (2021) - [j8]Ilan Shomorony
DNA-Based Storage: Models and Fundamental Limits. IEEE Trans. Inf. Theory 67(6): 3675-3689 (2021) - [c25]Reinhard Heckel, Fatih Furkan Yilmaz:
Early Stopping in Deep Networks: Double Descent and How to Eliminate it. ICLR 2021 - [c24]Mohammad Zalbagi Darestani, Akshay S. Chaudhari, Reinhard Heckel:
Measuring Robustness in Deep Learning Based Compressive Sensing. ICML 2021: 2433-2444 - [c23]Zalan Fabian, Reinhard Heckel, Mahdi Soltanolkotabi
Data augmentation for deep learning based accelerated MRI reconstruction with limited data. ICML 2021: 3057-3067 - [c22]Konstantin Donhauser, Alexandru Tifrea, Michael Aerni, Reinhard Heckel, Fanny Yang:
Interpolation can hurt robust generalization even when there is no noise. NeurIPS 2021: 23465-23477 - [c21]Zhenwei Dai, Aditya Desai, Reinhard Heckel, Anshumali Shrivastava:
Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix. SIGMOD Conference 2021: 352-364 - [i41]Zalan Fabian, Reinhard Heckel, Mahdi Soltanolkotabi:
Data augmentation for deep learning based accelerated MRI reconstruction with limited data. CoRR abs/2106.14947 (2021) - [i40]Konstantin Donhauser, Alexandru Tifrea, Michael Aerni, Reinhard Heckel, Fanny Yang:
Interpolation can hurt robust generalization even when there is no noise. CoRR abs/2108.02883 (2021) - [i39]Samuel Rey
Untrained Graph Neural Networks for Denoising. CoRR abs/2109.11700 (2021) - [i38]Kel Levick, Reinhard Heckel, Ilan Shomorony:
Achieving the Capacity of a DNA Storage Channel with Linear Coding Schemes. CoRR abs/2112.01630 (2021) - [i37]Reinhard Heckel:
Provable Continual Learning via Sketched Jacobian Approximations. CoRR abs/2112.05095 (2021) - 2020
- [c20]Seiyun Shin, Reinhard Heckel, Ilan Shomorony:
Capacity of the Erasure Shuffling Channel. ICASSP 2020: 8841-8845 - [c19]Reinhard Heckel, Mahdi Soltanolkotabi
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators. ICLR 2020 - [c18]Reinhard Heckel, Mahdi Soltanolkotabi
Compressive sensing with un-trained neural networks: Gradient descent finds a smooth approximation. ICML 2020: 4149-4158 - [i36]Ilan Shomorony, Reinhard Heckel:
DNA-Based Storage: Models and Fundamental Limits. CoRR abs/2001.06311 (2020) - [i35]Max Daniels, Paul Hand, Reinhard Heckel:
Reducing the Representation Error of GAN Image Priors Using the Deep Decoder. CoRR abs/2001.08747 (2020) - [i34]Reinhard Heckel, Mahdi Soltanolkotabi:
Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximation. CoRR abs/2005.03991 (2020) - [i33]Mohammad Zalbagi Darestani, Reinhard Heckel:
Can Un-trained Neural Networks Compete with Trained Neural Networks at Image Reconstruction? CoRR abs/2007.02471 (2020) - [i32]Reinhard Heckel, Fatih Furkan Yilmaz:
Early Stopping in Deep Networks: Double Descent and How to Eliminate it. CoRR abs/2007.10099 (2020) - [i31]Zhenwei Dai, Aditya Desai, Reinhard Heckel, Anshumali Shrivastava:
Active Sampling Count Sketch (ASCS) for Online Sparse Estimation of a Trillion Scale Covariance Matrix. CoRR abs/2010.15951 (2020)
2010 – 2019
- 2019
- [j7]Michail Vlachos
Addressing Interpretability and Cold-Start in Matrix Factorization for Recommender Systems. IEEE Trans. Knowl. Data Eng. 31(7): 1253-1266 (2019) - [c17]Daniel LeJeune, Reinhard Heckel, Richard G. Baraniuk:
Adaptive Estimation for Approximate k-Nearest-Neighbor Computations. AISTATS 2019: 3099-3107 - [c16]Frank Ong, Reinhard Heckel, Kannan Ramchandran:
A Fast and Robust Paradigm for Fourier Compressed Sensing Based on Coded Sampling. ICASSP 2019: 5117-5121 - [c15]Reinhard Heckel, Paul Hand:
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks. ICLR (Poster) 2019 - [c14]Ilan Shomorony, Reinhard Heckel:
Capacity Results for the Noisy Shuffling Channel. ISIT 2019: 762-766 - [i30]Daniel LeJeune, Richard G. Baraniuk, Reinhard Heckel:
Adaptive Estimation for Approximate k-Nearest-Neighbor Computations. CoRR abs/1902.09465 (2019) - [i29]Ilan Shomorony, Reinhard Heckel:
Capacity Results for the Noisy Shuffling Channel. CoRR abs/1902.10832 (2019) - [i28]Reinhard Heckel:
Regularizing linear inverse problems with convolutional neural networks. CoRR abs/1907.03100 (2019) - [i27]Zhenwei Dai, Reinhard Heckel:
Channel Normalization in Convolutional Neural Network avoids Vanishing Gradients. CoRR abs/1907.09539 (2019) - [i26]Fatih Furkan Yilmaz, Reinhard Heckel:
Leveraging inductive bias of neural networks for learning without explicit human annotations. CoRR abs/1910.09055 (2019) - [i25]Reinhard Heckel, Mahdi Soltanolkotabi:
Denoising and Regularization via Exploiting the Structural Bias of Convolutional Generators. CoRR abs/1910.14634 (2019) - 2018
- [j6]Reinhard Heckel
Generalized Line Spectral Estimation via Convex Optimization. IEEE Trans. Inf. Theory 64(6): 4001-4023 (2018) - [c13]Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright:
Approximate ranking from pairwise comparisons. AISTATS 2018: 1057-1066 - [i24]Reinhard Heckel, Max Simchowitz, Kannan Ramchandran, Martin J. Wainwright:
Approximate Ranking from Pairwise Comparisons. CoRR abs/1801.01253 (2018) - [i23]Reinhard Heckel, Gediminas Mikutis, Robert N. Grass:
A Characterization of the DNA Data Storage Channel. CoRR abs/1803.03322 (2018) - [i22]Reinhard Heckel, Wen Huang, Paul Hand, Vladislav Voroninski:
Deep Denoising: Rate-Optimal Recovery of Structured Signals with a Deep Prior. CoRR abs/1805.08855 (2018) - [i21]Christopher A. Metzler, Ali Mousavi, Reinhard Heckel, Richard G. Baraniuk:
Unsupervised Learning with Stein's Unbiased Risk Estimator. CoRR abs/1805.10531 (2018) - [i20]Reinhard Heckel:
Super-resolution radar imaging via convex optimization. CoRR abs/1810.03018 (2018) - [i19]Reinhard Heckel, Paul Hand:
Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networks. CoRR abs/1810.03982 (2018) - 2017
- [c12]Reinhard Heckel, Michail Vlachos
Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering. ICDE 2017: 1033-1044 - [c11]Reinhard Heckel, Kannan Ramchandran:
The Sample Complexity of Online One-Class Collaborative Filtering. ICML 2017: 1452-1460 - [c10]Reinhard Heckel, Ilan Shomorony, Kannan Ramchandran, David N. C. Tse:
Fundamental limits of DNA storage systems. ISIT 2017: 3130-3134 - [c9]Reinhard Heckel, Michail Vlachos:
Private and Right-Protected Big Data Publication: An Analysis. SDM 2017: 660-668 - [i18]Reinhard Heckel, Ilan Shomorony, Kannan Ramchandran, David N. C. Tse:
Fundamental Limits of DNA Storage Systems. CoRR abs/1705.04732 (2017) - [i17]Reinhard Heckel, Kannan Ramchandran:
The Sample Complexity of Online One-Class Collaborative Filtering. CoRR abs/1706.00061 (2017) - [i16]Nick Antipa, Grace Kuo, Reinhard Heckel, Ben Mildenhall, Emrah Bostan, Ren Ng, Laura Waller:
DiffuserCam: Lensless Single-exposure 3D Imaging. CoRR abs/1710.02134 (2017) - 2016
- [j5]Michail Vlachos
Toward interpretable predictive models in B2B recommender systems. IBM J. Res. Dev. 60(5/6): 11:1-11:12 (2016) - [c8]Reinhard Heckel:
Super-resolution MIMO radar. ISIT 2016: 1416-1420 - [i15]Reinhard Heckel, Michail Vlachos:
Interpretable recommendations via overlapping co-clusters. CoRR abs/1604.02071 (2016) - [i14]Reinhard Heckel:
Super-Resolution MIMO Radar. CoRR abs/1605.03230 (2016) - [i13]Reinhard Heckel, Nihar B. Shah, Kannan Ramchandran, Martin J. Wainwright:
Active Ranking from Pairwise Comparisons and the Futility of Parametric Assumptions. CoRR abs/1606.08842 (2016) - [i12]Reinhard Heckel, Mahdi Soltanolkotabi:
Generalized Line Spectral Estimation via Convex Optimization. CoRR abs/1609.08198 (2016) - 2015
- [j4]Reinhard Heckel, Helmut Bölcskei:
Robust Subspace Clustering via Thresholding. IEEE Trans. Inf. Theory 61(11): 6320-6342 (2015) - [i11]Reinhard Heckel, Michael Tschannen, Helmut Bölcskei:
Dimensionality-reduced subspace clustering. CoRR abs/1507.07105 (2015) - 2014
- [b1]Reinhard Heckel:
Sparse signal processing: subspace clustering and system identification. ETH Zurich, Hartung-Gorre 2014, ISBN 978-3-86628-513-2, pp. 1-181 - [c7]Alexander Jung
Compressive nonparametric graphical model selection for time series. ICASSP 2014: 769-773 - [c6]Reinhard Heckel, Eirikur Agustsson, Helmut Bölcskei:
Neighborhood selection for thresholding-based subspace clustering. ICASSP 2014: 6761-6765 - [c5]Reinhard Heckel, Michael Tschannen, Helmut Bölcskei:
Subspace clustering of dimensionality-reduced data. ISIT 2014: 2997-3001 - [i10]Reinhard Heckel, Eirikur Agustsson, Helmut Bölcskei:
Neighborhood Selection for Thresholding-based Subspace Clustering. CoRR abs/1403.3438 (2014) - [i9]Reinhard Heckel, Michael Tschannen, Helmut Bölcskei:
Subspace clustering of dimensionality-reduced data. CoRR abs/1404.6818 (2014) - [i8]Reinhard Heckel, Veniamin I. Morgenshtern, Mahdi Soltanolkotabi:
Super-Resolution Radar. CoRR abs/1411.6272 (2014) - 2013
- [j3]Reinhard Heckel, Steffen Schober, Martin Bossert:
Harmonic analysis of Boolean networks: determinative power and perturbations. EURASIP J. Bioinform. Syst. Biol. 2013: 6 (2013) - [j2]Reinhard Heckel, Helmut Bölcskei:
Identification of Sparse Linear Operators. IEEE Trans. Inf. Theory 59(12): 7985-8000 (2013) - [c4]Reinhard Heckel, Helmut Bölcskei:
Subspace clustering via thresholding and spectral clustering. ICASSP 2013: 3263-3267 - [c3]Reinhard Heckel, Helmut Bölcskei:
Noisy subspace clustering via thresholding. ISIT 2013: 1382-1386 - [i7]Reinhard Heckel, Helmut Bölcskei:
Subspace Clustering via Thresholding and Spectral Clustering. CoRR abs/1303.3716 (2013) - [i6]Reinhard Heckel, Helmut Bölcskei:
Noisy Subspace Clustering via Thresholding. CoRR abs/1305.3486 (2013) - [i5]Reinhard Heckel, Helmut Bölcskei:
Robust Subspace Clustering via Thresholding. CoRR abs/1307.4891 (2013) - 2012
- [c2]Reinhard Heckel, Helmut Bölcskei:
Joint sparsity with different measurement matrices. Allerton Conference 2012: 698-702 - [i4]Reinhard Heckel, Helmut Bölcskei:
Identification of Sparse Linear Operators. CoRR abs/1209.5187 (2012) - [i3]Reinhard Heckel, Helmut Bölcskei:
Joint Sparsity with Different Measurement Matrices. CoRR abs/1210.2272 (2012) - 2011
- [j1]Steffen Schober, David Kracht, Reinhard Heckel, Martin Bossert:
Detecting controlling nodes of boolean regulatory networks. EURASIP J. Bioinform. Syst. Biol. 2011: 6 (2011) - [c1]Reinhard Heckel, Helmut Bölcskei:
Compressive identification of linear operators. ISIT 2011: 1412-1416 - [i2]Reinhard Heckel, Helmut Bölcskei:
Compressive Identification of Linear Operators. CoRR abs/1105.5215 (2011) - [i1]Reinhard Heckel, Steffen Schober, Martin Bossert:
Harmonic Analysis of Boolean Networks: Determinative Power and Perturbations. CoRR abs/1109.0807 (2011)
Coauthor Index
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