Jeremias Sulam

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 71

Top Topics

Dictionary Learning

Weakly Supervised Learning

Top Venues

IEEE Trans. Signal Process.

SIAM J. Math. Data Sci.

Publications

Nelson Goldenstein, Jeremias Sulam, Yaniv Romano
Pivotal Auto-Encoder via Self-Normalizing ReLU. CoRR (2024)
Zhenghan Fang, Sam Buchanan, Jeremias Sulam
What's in a Prior? Learned Proximal Networks for Inverse Problems. ICLR (2024)
Nelson Goldenstein, Jeremias Sulam, Yaniv Romano
Pivotal Auto-Encoder via Self-Normalizing ReLU. IEEE Trans. Signal Process. 72 (2024)
Ambar Pal, René Vidal, Jeremias Sulam
Certified Robustness against Sparse Adversarial Perturbations via Data Localization. CoRR (2024)
Jacopo Teneggi, Jeremias Sulam
I Bet You Did Not Mean That: Testing Semantic Importance via Betting. CoRR (2024)
Ramchandran Muthukumar, Jeremias Sulam
Sparsity-aware generalization theory for deep neural networks. COLT (2023)
Jacopo Teneggi, Matthew Tivnan, J. Webster Stayman, Jeremias Sulam
How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control. CoRR (2023)
Ambar Pal, Jeremias Sulam
Understanding Noise-Augmented Training for Randomized Smoothing. CoRR (2023)
Ambar Pal, Jeremias Sulam, René Vidal
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness. CoRR (2023)
Jacopo Teneggi, Beepul Bharti, Yaniv Romano, Jeremias Sulam
SHAP-XRT: The Shapley Value Meets Conditional Independence Testing. Trans. Mach. Learn. Res. 2023 (2023)
Ambar Pal, Jeremias Sulam
Understanding Noise-Augmented Training for Randomized Smoothing. Trans. Mach. Learn. Res. 2023 (2023)
Ambar Pal, Jeremias Sulam, René Vidal
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness. NeurIPS (2023)
Jacopo Teneggi, Alexandre Luster, Jeremias Sulam
Fast Hierarchical Games for Image Explanations. IEEE Trans. Pattern Anal. Mach. Intell. 45 (4) (2023)
Zhenghan Fang, Sam Buchanan, Jeremias Sulam
What's in a Prior? Learned Proximal Networks for Inverse Problems. CoRR (2023)
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)
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 (2023)
Ramchandran Muthukumar, Jeremias Sulam
Sparsity-aware generalization theory for deep neural networks. CoRR (2023)
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
* and magnetic susceptibility at 0.3 mm in-plane resolution at 7 T. NeuroImage 270 (2023)
Ramchandran Muthukumar, Jeremias Sulam
Adversarial Robustness of Sparse Local Lipschitz Predictors. SIAM J. Math. Data Sci. 5 (4) (2023)
Beepul Bharti, Paul H. Yi, Jeremias Sulam
Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors. NeurIPS (2023)
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)
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 (2022)
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) (2022)
Ramchandran Muthukumar, Jeremias Sulam
Adversarial robustness of sparse local Lipschitz predictors. CoRR (2022)
Jeremias Sulam, Chong You, Zhihui Zhu
Recovery and Generalization in Over-Realized Dictionary Learning. J. Mach. Learn. Res. 23 (2022)
Jeffrey A. Ruffolo, Jeremias Sulam, Jeffrey J. Gray
Antibody structure prediction using interpretable deep learning. Patterns 3 (2) (2022)
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 (2022)
Jacopo Teneggi, Beepul Bharti, Yaniv Romano, Jeremias Sulam
From Shapley back to Pearson: Hypothesis Testing via the Shapley Value. CoRR (2022)
Joshua Agterberg, Jeremias Sulam
Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms. AISTATS (2022)
Joshua T. Vogelstein, Timothy 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 Platt, Lyle 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 (2022)
Beepul Bharti, Paul Yi, Jeremias Sulam
Estimating and Controlling for Fairness via Sensitive Attribute Predictors. CoRR (2022)
Jacopo Teneggi, Alexandre Luster, Jeremias Sulam
Fast Hierarchical Games for Image Explanations. CoRR (2021)
Jeffrey A. Ruffolo, Jeffrey J. Gray, Jeremias Sulam
Deciphering antibody affinity maturation with language models and weakly supervised learning. CoRR (2021)
Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu
A Geometric Analysis of Neural Collapse with Unconstrained Features. CoRR (2021)
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)
Zhenzhen Wang, Aleksander S. Popel, Jeremias Sulam
Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images. CoRR (2021)
Jeremias Sulam, Ramchandran Muthukumar, Raman Arora
Adversarial Robustness of Supervised Sparse Coding. CoRR (2020)
Kuo-Wei Lai, Manisha Aggarwal, Peter C. M. van Zijl, Xu Li, Jeremias Sulam
Learned Proximal Networks for Quantitative Susceptibility Mapping. CoRR (2020)
Jeremias Sulam, Ramchandran Muthukumar, Raman Arora
Adversarial Robustness of Supervised Sparse Coding. NeurIPS (2020)
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) (2020)
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) (2020)
Jeremias Sulam, Chong You, Zhihui Zhu
Recovery and Generalization in Over-Realized Dictionary Learning. CoRR (2020)
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray
Deep Learning in Protein Structural Modeling and Design. CoRR (2020)
Kuo-Wei Lai, Manisha Aggarwal, Peter C. M. van Zijl, Xu Li, Jeremias Sulam
Learned Proximal Networks for Quantitative Susceptibility Mapping. MICCAI (2) (2020)
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) (2020)
Guilherme França, Jeremias Sulam, Daniel P. Robinson, René Vidal
Conformal Symplectic and Relativistic Optimization. NeurIPS (2020)
Ives Rey-Otero, Jeremias Sulam, Michael Elad
Variations on the Convolutional Sparse Coding Model. IEEE Trans. Signal Process. 68 (2020)
Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray
Deep Learning in Protein Structural Modeling and Design. Patterns 1 (9) (2020)
Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau
Learning to solve TV regularised problems with unrolled algorithms. NeurIPS (2020)
Ev Zisselman, Jeremias Sulam, Michael Elad
A Local Block Coordinate Descent Algorithm for the CSC Model. CVPR (2019)
Aviad Aberdam, Jeremias Sulam, Michael Elad
Multi-Layer Sparse Coding: The Holistic Way. SIAM J. Math. Data Sci. 1 (1) (2019)
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) (2019)
Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad
Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. IEEE Trans. Signal Process. 66 (15) (2018)
Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad
Improving Pursuit Algorithms Using Stochastic Resonance. CoRR (2018)
Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad
Projecting on to the Multi-Layer Convolutional Sparse Coding Model. ICASSP (2018)
Ev Zisselman, Jeremias Sulam, Michael Elad
A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model. CoRR (2018)
Jeremias Sulam, Aviad Aberdam, Michael Elad
On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks. CoRR (2018)
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) (2018)
Aviad Aberdam, Jeremias Sulam, Michael Elad
Multi Layer Sparse Coding: the Holistic Way. CoRR (2018)
Vardan Papyan, Jeremias Sulam, Michael Elad
Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding. CoRR (2017)
Vardan Papyan, Jeremias Sulam, Michael Elad
Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding. IEEE Trans. Signal Process. 65 (21) (2017)
Jeremias Sulam, Yaniv Romano, Ronen Talmon
Dynamical system classification with diffusion embedding for ECG-based person identification. Signal Process. 130 (2017)
Jeremias Sulam, Rami Ben-Ari, Pavel Kisilev
Maximizing AUC with Deep Learning for Classification of Imbalanced Mammogram Datasets. VCBM (2017)
Vardan Papyan, Yaniv Romano, Michael Elad, Jeremias Sulam
Convolutional Dictionary Learning via Local Processing. ICCV (2017)
Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad
Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning. CoRR (2017)
Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad
Convolutional Dictionary Learning via Local Processing. CoRR (2017)
Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, Michael Elad
Trainlets: Dictionary Learning in High Dimensions. IEEE Trans. Signal Process. 64 (12) (2016)
Jeremias Sulam, Michael Elad
Large Inpainting of Face Images With Trainlets. IEEE Signal Process. Lett. 23 (12) (2016)
Vardan Papyan, Jeremias Sulam, Michael Elad
Working Locally Thinking Globally - Part II: Stability and Algorithms for Convolutional Sparse Coding. CoRR (2016)
Vardan Papyan, Jeremias Sulam, Michael Elad
Working Locally Thinking Globally - Part I: Theoretical Guarantees for Convolutional Sparse Coding. CoRR (2016)
Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, Michael Elad
Trainlets: Dictionary Learning in High Dimensions. CoRR (2016)
Javier S. Turek, Jeremias Sulam, Michael Elad, Irad Yavneh
Fusion of ultrasound harmonic imaging with clutter removal using sparse signal separation. ICASSP (2015)
Jeremias Sulam, Boaz Ophir, Michael Elad
Image denoising through multi-scale learnt dictionaries. ICIP (2014)
Jeremias Sulam, Michael Elad
Expected Patch Log Likelihood with a Sparse Prior. EMMCVPR (2014)