Jeremias Sulam

According to our database, Jeremias Sulam authored at least 53 papers between 2014 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Adversarial Robustness of Sparse Local Lipschitz Predictors.

Ramchandran Muthukumar, Jeremias Sulam

SIAM J. Math. Data Sci., December, 2023

Fast Hierarchical Games for Image Explanations.

Jacopo Teneggi, Alexandre Luster, Jeremias Sulam

IEEE Trans. Pattern Anal. Mach. Intell., April, 2023

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.

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

NeuroImage, April, 2023

Understanding Noise-Augmented Training for Randomized Smoothing.

Ambar Pal, Jeremias Sulam

Trans. Mach. Learn. Res., 2023

DeepSTI: Towards tensor reconstruction using fewer orientations in susceptibility tensor imaging.

Zhenghan Fang, Kuo-Wei Lai, Peter C. M. van Zijl, Xu Li, Jeremias Sulam

Medical Image Anal., 2023

What's in a Prior? Learned Proximal Networks for Inverse Problems.

Zhenghan Fang, Sam Buchanan, Jeremias Sulam

CoRR, 2023

Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness.

Ambar Pal, Jeremias Sulam, René Vidal

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictors.

Beepul Bharti, Paul H. Yi, Jeremias Sulam

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

WaveSep: A Flexible Wavelet-Based Approach for Source Separation in Susceptibility Imaging.

Zhenghan Fang, Hyeong-Geol Shin, Peter C. M. van Zijl, Xu Li, Jeremias Sulam

Proceedings of the Machine Learning in Clinical Neuroimaging - 6th International Workshop, 2023

How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control.

Jacopo Teneggi, Matthew Tivnan, J. Webster Stayman, Jeremias Sulam

Proceedings of the International Conference on Machine Learning, 2023

Sparsity-aware generalization theory for deep neural networks.

Ramchandran Muthukumar, Jeremias Sulam

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

2022

Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images.

Zhenzhen Wang, Carla Saoud, Sintawat Wangsiricharoen, Aaron W. James, Aleksander S. Popel, Jeremias Sulam

IEEE Trans. Medical Imaging, 2022

Antibody structure prediction using interpretable deep learning.

Jeffrey A. Ruffolo, Jeremias Sulam, Jeffrey J. Gray

Patterns, 2022

Recovery and Generalization in Over-Realized Dictionary Learning.

Jeremias Sulam, Chong You, Zhihui Zhu

J. Mach. Learn. Res., 2022

Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT.

Jacopo Teneggi, Paul H. Yi, Jeremias Sulam

CoRR, 2022

Estimating and Controlling for Fairness via Sensitive Attribute Predictors.

Beepul Bharti, Paul H. Yi, Jeremias Sulam

CoRR, 2022

From Shapley back to Pearson: Hypothesis Testing via the Shapley Value.

Jacopo Teneggi, Beepul Bharti, Yaniv Romano, Jeremias Sulam

CoRR, 2022

Prospective Learning: Back to the Future.

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 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

CoRR, 2022

Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms.

Joshua Agterberg, Jeremias Sulam

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Deciphering antibody affinity maturation with language models and weakly supervised learning.

Jeffrey A. Ruffolo, Jeffrey J. Gray, Jeremias Sulam

CoRR, 2021

Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images.

Zhenzhen Wang, Aleksander S. Popel, Jeremias Sulam

CoRR, 2021

A Geometric Analysis of Neural Collapse with Unconstrained Features.

Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Variations on the Convolutional Sparse Coding Model.

Ives Rey-Otero, Jeremias Sulam, Michael Elad

IEEE Trans. Signal Process., 2020

Deep Learning in Protein Structural Modeling and Design.

Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray

Patterns, 2020

On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks.

Jeremias Sulam, Aviad Aberdam, Amir Beck, Michael Elad

IEEE Trans. Pattern Anal. Mach. Intell., 2020

Adversarial Noise Attacks of Deep Learning Architectures: Stability Analysis via Sparse-Modeled Signals.

Yaniv Romano, Aviad Aberdam, Jeremias Sulam, Michael Elad

J. Math. Imaging Vis., 2020

Geometric potentials from deep learning improve prediction of CDR H3 loop structures.

Jeffrey A. Ruffolo, Carlos Guerra, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray

Bioinform., 2020

Adversarial Robustness of Supervised Sparse Coding.

Jeremias Sulam, Ramchandran Muthukumar, Raman Arora

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Conformal Symplectic and Relativistic Optimization.

Guilherme França, Jeremias Sulam, Daniel P. Robinson, René Vidal

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learning to solve TV regularised problems with unrolled algorithms.

Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Learned Proximal Networks for Quantitative Susceptibility Mapping.

Kuo-Wei Lai, Manisha Aggarwal, Peter C. M. van Zijl, Xu Li, Jeremias Sulam

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

2019

MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance.

Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad

IEEE Trans. Signal Process., 2019

Multi-Layer Sparse Coding: The Holistic Way.

Aviad Aberdam, Jeremias Sulam, Michael Elad

SIAM J. Math. Data Sci., 2019

A Local Block Coordinate Descent Algorithm for the CSC Model.

Ev Zisselman, Jeremias Sulam, Michael Elad

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019

2018

From Local to Global Sparse Modeling.

Jeremias Sulam

PhD thesis, 2018

Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning.

Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad

IEEE Trans. Signal Process., 2018

Theoretical Foundations of Deep Learning via Sparse Representations: A Multilayer Sparse Model and Its Connection to Convolutional Neural Networks.

Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad

IEEE Signal Process. Mag., 2018

A Local Block Coordinate Descent Algorithm for the Convolutional Sparse Coding Model.

Ev Zisselman, Jeremias Sulam, Michael Elad

CoRR, 2018

Improving Pursuit Algorithms Using Stochastic Resonance.

Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad

CoRR, 2018

On Multi-Layer Basis Pursuit, Efficient Algorithms and Convolutional Neural Networks.

Jeremias Sulam, Aviad Aberdam, Michael Elad

CoRR, 2018

Projecting on to the Multi-Layer Convolutional Sparse Coding Model.

Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad

Proceedings of the 2018 IEEE International Conference on Acoustics, 2018

2017

Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding.

Vardan Papyan, Jeremias Sulam, Michael Elad

IEEE Trans. Signal Process., 2017

Dynamical system classification with diffusion embedding for ECG-based person identification.

Jeremias Sulam, Yaniv Romano, Ronen Talmon

Signal Process., 2017

Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning.

Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad

CoRR, 2017

Maximizing AUC with Deep Learning for Classification of Imbalanced Mammogram Datasets.

Jeremias Sulam, Rami Ben-Ari, Pavel Kisilev

Proceedings of the VCBM 17: Eurographics Workshop on Visual Computing for Biology and Medicine, 2017

Convolutional Dictionary Learning via Local Processing.

Vardan Papyan, Yaniv Romano, Michael Elad, Jeremias Sulam

Proceedings of the IEEE International Conference on Computer Vision, 2017

2016

Trainlets: Dictionary Learning in High Dimensions.

Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, Michael Elad

IEEE Trans. Signal Process., 2016

Large Inpainting of Face Images With Trainlets.

Jeremias Sulam, Michael Elad

IEEE Signal Process. Lett., 2016

Working Locally Thinking Globally - Part II: Stability and Algorithms for Convolutional Sparse Coding.

Vardan Papyan, Jeremias Sulam, Michael Elad

CoRR, 2016

Working Locally Thinking Globally - Part I: Theoretical Guarantees for Convolutional Sparse Coding.

Vardan Papyan, Jeremias Sulam, Michael Elad

CoRR, 2016

2015

Fusion of ultrasound harmonic imaging with clutter removal using sparse signal separation.

Javier S. Turek, Jeremias Sulam, Michael Elad, Irad Yavneh

Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014

Image denoising through multi-scale learnt dictionaries.

Jeremias Sulam, Boaz Ophir, Michael Elad

Proceedings of the 2014 IEEE International Conference on Image Processing, 2014

Expected Patch Log Likelihood with a Sparse Prior.

Jeremias Sulam, Michael Elad

Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2014