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2020 – today
- 2025
[i34]Ramchandran Muthukumar, Ambar Pal, Jeremias Sulam, René Vidal:
Disentangling Safe and Unsafe Corruptions via Anisotropy and Locality. CoRR abs/2501.18098 (2025)- 2024
- [j23]Nelson Goldenstein
, Jeremias Sulam, Yaniv Romano:
Pivotal Auto-Encoder via Self-Normalizing ReLU. IEEE Trans. Signal Process. 72: 3201-3212 (2024) - [c21]Zhenghan Fang, Sam Buchanan, Jeremias Sulam:
What's in a Prior? Learned Proximal Networks for Inverse Problems. ICLR 2024 - [c20]Shmuel Orenstein, Zhenghan Fang, Hyeong-Geol Shin, Peter C. M. van Zijl, Xu Li, Jeremias Sulam:
ProxiMO: Proximal Multi-operator Networks for Quantitative Susceptibility Mapping. MLCN@MICCAI 2024: 13-23 - [c19]Jacopo Teneggi, Jeremias Sulam:
Testing Semantic Importance via Betting. NeurIPS 2024 - [i33]Ambar Pal, René Vidal, Jeremias Sulam:
Certified Robustness against Sparse Adversarial Perturbations via Data Localization. CoRR abs/2405.14176 (2024) - [i32]Jacopo Teneggi, Jeremias Sulam:
I Bet You Did Not Mean That: Testing Semantic Importance via Betting. CoRR abs/2405.19146 (2024) - [i31]Nelson Goldenstein, Jeremias Sulam, Yaniv Romano:
Pivotal Auto-Encoder via Self-Normalizing ReLU. CoRR abs/2406.16052 (2024) - [i30]Beepul Bharti, Paul H. Yi, Jeremias Sulam:
Sufficient and Necessary Explanations (and What Lies in Between). CoRR abs/2409.20427 (2024) - 2023
- [j22]Zhenghan Fang, Kuo-Wei Lai, Peter C. M. van Zijl, Xu Li, Jeremias Sulam:
DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging. Medical Image Anal. 87: 102829 (2023) - [j21]Peter van Gelderen, Xu Li, Jacco A. de Zwart, Erin S. Beck, Serhat V. Okar, Yujia Huang, KuoWei Lai, Jeremias Sulam, Peter C. M. van Zijl, Daniel S. Reich, Jeff H. Duyn, Jiaen Liu:
Effect of motion, cortical orientation and spatial resolution on quantitative imaging of cortical R2* and magnetic susceptibility at 0.3 mm in-plane resolution at 7 T. NeuroImage 270: 119992 (2023) - [j20]Jacopo Teneggi, Alexandre Luster, Jeremias Sulam:
Fast Hierarchical Games for Image Explanations. IEEE Trans. Pattern Anal. Mach. Intell. 45(4): 4494-4503 (2023) - [j19]Ramchandran Muthukumar, Jeremias Sulam:
Adversarial Robustness of Sparse Local Lipschitz Predictors. SIAM J. Math. Data Sci. 5(4): 920-948 (2023) - [j18]Ambar Pal, Jeremias Sulam:
Understanding Noise-Augmented Training for Randomized Smoothing. Trans. Mach. Learn. Res. 2023 (2023) - [j17]Jacopo Teneggi, Beepul Bharti, Yaniv Romano, Jeremias Sulam:
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing. Trans. Mach. Learn. Res. 2023 (2023) - [c18]Ramchandran Muthukumar, Jeremias Sulam:
Sparsity-aware generalization theory for deep neural networks. COLT 2023: 5311-5342 - [c17]Jacopo Teneggi, Matthew Tivnan, J. Webster Stayman, Jeremias Sulam:
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control. ICML 2023: 33940-33960 - [c16]Zhenghan Fang, Hyeong-Geol Shin, Peter C. M. van Zijl, Xu Li, Jeremias Sulam:
WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility Imaging. MLCN@MICCAI 2023: 56-66 - [c15]Beepul Bharti, Paul H. Yi, Jeremias Sulam:
Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors. NeurIPS 2023 - [c14]Ambar Pal, Jeremias Sulam, René Vidal:
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness. NeurIPS 2023 - [i29]Jacopo Teneggi, Matthew Tivnan, J. Webster Stayman, Jeremias Sulam:
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control. CoRR abs/2302.03791 (2023) - [i28]Ambar Pal, Jeremias Sulam:
Understanding Noise-Augmented Training for Randomized Smoothing. CoRR abs/2305.04746 (2023) - [i27]Ramchandran Muthukumar, Jeremias Sulam:
Sparsity-aware generalization theory for deep neural networks. CoRR abs/2307.00426 (2023) - [i26]Ambar Pal, Jeremias Sulam, René Vidal:
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness. CoRR abs/2309.16096 (2023) - [i25]Zhenghan Fang, Sam Buchanan, Jeremias Sulam:
What's in a Prior? Learned Proximal Networks for Inverse Problems. CoRR abs/2310.14344 (2023) - 2022
- [j16]Jeremias Sulam, Chong You, Zhihui Zhu:
Recovery and Generalization in Over-Realized Dictionary Learning. J. Mach. Learn. Res. 23: 135:1-135:23 (2022) - [j15]Jeffrey A. Ruffolo, Jeremias Sulam, Jeffrey J. Gray:
Antibody structure prediction using interpretable deep learning. Patterns 3(2): 100406 (2022) - [j14]Zhenzhen Wang, Carla Saoud, Sintawat Wangsiricharoen, Aaron W. James, Aleksander S. Popel, Jeremias Sulam:
Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images. IEEE Trans. Medical Imaging 41(12): 3952-3968 (2022) - [c13]Joshua Agterberg, Jeremias Sulam:
Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms. AISTATS 2022: 6591-6629 - [i24]Joshua T. Vogelstein, Timothy D. Verstynen, Konrad P. Kording, Leyla Isik, John W. Krakauer, Ralph Etienne-Cummings, Elizabeth L. Ogburn, Carey E. Priebe, Randal C. Burns, Kwame S. Kutten, James J. Knierim, James B. Potash, Thomas Hartung, Lena Smirnova, Paul Worley, Alena V. Savonenko, Ian Phillips, Michael I. Miller, René Vidal, Jeremias Sulam, Adam Charles, Noah J. Cowan, Maxim Bichuch, Archana Venkataraman, Chen Li, Nitish V. Thakor, Justus M. Kebschull, Marilyn S. Albert, Jinchong Xu, Marshall G. Hussain Shuler, Brian Caffo, J. Tilak Ratnanather, Ali Geisa, Seung-Eon Roh, Eva Yezerets, Meghana Madhyastha, Javier J. How, Tyler M. Tomita, Jayanta Dey, Ningyuan Huang, Jong M. Shin, Kaleab Alemayehu Kinfu, Pratik Chaudhari, Ben Baker, Anna Schapiro, Dinesh Jayaraman, Eric Eaton, Michael L. Platt, Lyle H. Ungar, Leila Wehbe, Ádám Kepecs, Amy Christensen, Onyema Osuagwu, Bing Brunton, Brett Mensh, Alysson R. Muotri, Gabriel A. Silva, Francesca Puppo, Florian Engert, Elizabeth Hillman, Julia Brown, Chris White, Weiwei Yang:
Prospective Learning: Back to the Future. CoRR abs/2201.07372 (2022) - [i23]Ramchandran Muthukumar, Jeremias Sulam:
Adversarial robustness of sparse local Lipschitz predictors. CoRR abs/2202.13216 (2022) - [i22]Jacopo Teneggi, Beepul Bharti, Yaniv Romano, Jeremias Sulam:
From Shapley back to Pearson: Hypothesis Testing via the Shapley Value. CoRR abs/2207.07038 (2022) - [i21]Beepul Bharti, Paul H. Yi, Jeremias Sulam:
Estimating and Controlling for Fairness via Sensitive Attribute Predictors. CoRR abs/2207.12497 (2022) - [i20]Zhenghan Fang, Kuo-Wei Lai, Peter C. M. van Zijl, Xu Li, Jeremias Sulam:
DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging. CoRR abs/2209.04504 (2022) - [i19]Jacopo Teneggi, Paul H. Yi, Jeremias Sulam:
Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT. CoRR abs/2211.15924 (2022) - 2021
- [c12]Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu:
A Geometric Analysis of Neural Collapse with Unconstrained Features. NeurIPS 2021: 29820-29834 - [i18]Jacopo Teneggi, Alexandre Luster, Jeremias Sulam:
Fast Hierarchical Games for Image Explanations. CoRR abs/2104.06164 (2021) - [i17]Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu:
A Geometric Analysis of Neural Collapse with Unconstrained Features. CoRR abs/2105.02375 (2021) - [i16]Zhenzhen Wang, Aleksander S. Popel, Jeremias Sulam:
Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images. CoRR abs/2109.10778 (2021) - [i15]Jeffrey A. Ruffolo, Jeffrey J. Gray, Jeremias Sulam:
Deciphering antibody affinity maturation with language models and weakly supervised learning. CoRR abs/2112.07782 (2021) - 2020
- [j13]Jeffrey A. Ruffolo, Carlos Guerra, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray:
Geometric potentials from deep learning improve prediction of CDR H3 loop structures. Bioinform. 36(Supplement-1): i268-i275 (2020) - [j12]Yaniv Romano, Aviad Aberdam, Jeremias Sulam, Michael Elad:
Adversarial Noise Attacks of Deep Learning Architectures: Stability Analysis via Sparse-Modeled Signals. J. Math. Imaging Vis. 62(3): 313-327 (2020) - [j11]Jeremias Sulam, Aviad Aberdam, Amir Beck, Michael Elad:
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks. IEEE Trans. Pattern Anal. Mach. Intell. 42(8): 1968-1980 (2020) - [j10]Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray:
Deep Learning in Protein Structural Modeling and Design. Patterns 1(9): 100142 (2020) - [j9]Ives Rey-Otero, Jeremias Sulam, Michael Elad:
Variations on the Convolutional Sparse Coding Model. IEEE Trans. Signal Process. 68: 519-528 (2020) - [c11]Kuo-Wei Lai, Manisha Aggarwal, Peter C. M. van Zijl, Xu Li, Jeremias Sulam:
Learned Proximal Networks for Quantitative Susceptibility Mapping. MICCAI (2) 2020: 125-135 - [c10]Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau:
Learning to solve TV regularised problems with unrolled algorithms. NeurIPS 2020 - [c9]Guilherme França, Jeremias Sulam, Daniel P. Robinson, René Vidal:
Conformal Symplectic and Relativistic Optimization. NeurIPS 2020 - [c8]Jeremias Sulam, Ramchandran Muthukumar, Raman Arora:
Adversarial Robustness of Supervised Sparse Coding. NeurIPS 2020 - [i14]Jeremias Sulam, Chong You, Zhihui Zhu:
Recovery and Generalization in Over-Realized Dictionary Learning. CoRR abs/2006.06179 (2020) - [i13]Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray:
Deep Learning in Protein Structural Modeling and Design. CoRR abs/2007.08383 (2020) - [i12]Kuo-Wei Lai, Manisha Aggarwal, Peter C. M. van Zijl, Xu Li, Jeremias Sulam:
Learned Proximal Networks for Quantitative Susceptibility Mapping. CoRR abs/2008.05024 (2020) - [i11]Jeremias Sulam, Ramchandran Muthukumar, Raman Arora:
Adversarial Robustness of Supervised Sparse Coding. CoRR abs/2010.12088 (2020)
2010 – 2019
- 2019
- [j8]Aviad Aberdam, Jeremias Sulam, Michael Elad:
Multi-Layer Sparse Coding: The Holistic Way. SIAM J. Math. Data Sci. 1(1): 46-77 (2019) - [j7]Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad:
MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance. IEEE Trans. Signal Process. 67(17): 4597-4610 (2019) - [c7]Ev Zisselman, Jeremias Sulam, Michael Elad:
A Local Block Coordinate Descent Algorithm for the CSC Model. CVPR 2019: 8208-8217 - 2018
- [b1]Jeremias Sulam:
From Local to Global Sparse Modeling. Technion - Israel Institute of Technology, Israel, 2018 - [j6]Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad:
Theoretical Foundations of Deep Learning via Sparse Representations: A Multilayer Sparse Model and Its Connection to Convolutional Neural Networks. IEEE Signal Process. Mag. 35(4): 72-89 (2018) - [j5]Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad:
Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. IEEE Trans. Signal Process. 66(15): 4090-4104 (2018) - [c6]Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad:
Projecting on to the Multi-Layer Convolutional Sparse Coding Model. ICASSP 2018: 6757-6761 - [i10]Aviad Aberdam, Jeremias Sulam, Michael Elad:
Multi Layer Sparse Coding: the Holistic Way. CoRR abs/1804.09788 (2018) - [i9]Jeremias Sulam, Aviad Aberdam, Michael Elad:
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks. CoRR abs/1806.00701 (2018) - [i8]Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad:
Improving Pursuit Algorithms Using Stochastic Resonance. CoRR abs/1806.10171 (2018) - [i7]Ev Zisselman, Jeremias Sulam, Michael Elad:
A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model. CoRR abs/1811.00312 (2018) - 2017
- [j4]Jeremias Sulam, Yaniv Romano, Ronen Talmon:
Dynamical system classification with diffusion embedding for ECG-based person identification. Signal Process. 130: 403-411 (2017) - [j3]Vardan Papyan, Jeremias Sulam, Michael Elad:
Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding. IEEE Trans. Signal Process. 65(21): 5687-5701 (2017) - [c5]Vardan Papyan, Yaniv Romano, Michael Elad, Jeremias Sulam:
Convolutional Dictionary Learning via Local Processing. ICCV 2017: 5306-5314 - [c4]Jeremias Sulam, Rami Ben-Ari, Pavel Kisilev:
Maximizing AUC with Deep Learning for Classification of Imbalanced Mammogram Datasets. VCBM 2017: 131-135 - [i6]Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad:
Convolutional Dictionary Learning via Local Processing. CoRR abs/1705.03239 (2017) - [i5]Vardan Papyan, Jeremias Sulam, Michael Elad:
Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding. CoRR abs/1707.06066 (2017) - [i4]Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad:
Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. CoRR abs/1708.08705 (2017) - 2016
- [j2]Jeremias Sulam, Michael Elad:
Large Inpainting of Face Images With Trainlets. IEEE Signal Process. Lett. 23(12): 1839-1843 (2016) - [j1]Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, Michael Elad:
Trainlets: Dictionary Learning in High Dimensions. IEEE Trans. Signal Process. 64(12): 3180-3193 (2016) - [i3]Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, Michael Elad:
Trainlets: Dictionary Learning in High Dimensions. CoRR abs/1602.00212 (2016) - [i2]Vardan Papyan, Jeremias Sulam, Michael Elad:
Working Locally Thinking Globally - Part I: Theoretical Guarantees for Convolutional Sparse Coding. CoRR abs/1607.02005 (2016) - [i1]Vardan Papyan, Jeremias Sulam, Michael Elad:
Working Locally Thinking Globally - Part II: Stability and Algorithms for Convolutional Sparse Coding. CoRR abs/1607.02009 (2016) - 2015
- [c3]Javier S. Turek, Jeremias Sulam, Michael Elad, Irad Yavneh:
Fusion of ultrasound harmonic imaging with clutter removal using sparse signal separation. ICASSP 2015: 793-797 - 2014
- [c2]Jeremias Sulam, Michael Elad:
Expected Patch Log Likelihood with a Sparse Prior. EMMCVPR 2014: 99-111 - [c1]Jeremias Sulam, Boaz Ophir, Michael Elad:
Image denoising through multi-scale learnt dictionaries. ICIP 2014: 808-812
Coauthor Index
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