Jeremias Sulam

发表

Prospective Learning: Back to the Future pdf

Eric Eaton, Nitish Thakor, Ralph Etienne-Cummings, 2022, ArXiv.

Working Locally Thinking Globally: Theoretical Guarantees for Convolutional Sparse Coding

Michael Elad, Jeremias Sulam, Vardan Papyan, 2017, IEEE Transactions on Signal Processing.

Antibody structure prediction using interpretable deep learning

Jeffrey J. Gray, Jeremias Sulam, Jeffrey A. Ruffolo, 2021, bioRxiv.

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

Jeffrey A. Ruffolo, Jeremias Sulam, J. J. Gray, 2020, Bioinformatics.

Antibody structure prediction using interpretable deep learning

Jeffrey A. Ruffolo, Jeremias Sulam, J. J. Gray, 2021, bioRxiv.

Learned Proximal Networks for Quantitative Susceptibility Mapping

Xu Li, Jeremias Sulam, Manisha Aggarwal, 2020, MICCAI.

Nonlinear slight parameter changes detection: a forecasting approach

Jeremias Sulam, F. Ingenier, E. Torres, 2012 .

Inpainting of Large Face Images with Trainlets (slides)

Michael Elad, Jeremias Sulam, Michael Elad, 2017 .

High Dimensional Dictionary Learning and Applications

Michael Elad, M. Zibulevsky, Jeremias Sulam, 2017 .

Fast Hierarchical Games for Image Explanations

Jeremias Sulam, Jacopo Teneggi, Alexandre Luster, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

Jeremias Sulam, A. Popel, Zhenzhen Wang, 2021, IEEE Transactions on Medical Imaging.

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

Jeremias Sulam, Yaniv Romano, Jacopo Teneggi, 2022, 2207.07038.

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

Jeremias Sulam, Yaniv Romano, Jacopo Teneggi, 2022, ArXiv.

DeepSTI: Towards Tensor Reconstruction using Fewer Orientations in Susceptibility Tensor Imaging

Jeremias Sulam, P. V. Zijl, Zhenghan Fang, 2022, Medical Image Anal..

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

Jeremias Sulam, Yaniv Romano, Jacopo Teneggi, 2022, ArXiv.

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

Jeremias Sulam, Jacopo Teneggi, P. Yi, 2022, ArXiv.

A Geometric Analysis of Neural Collapse with Unconstrained Features pdf

Zhihui Zhu, Chong You, Qing Qu, 2021, NeurIPS.

Recovery and Generalization in Over-Realized Dictionary Learning

Zhihui Zhu, Jeremias Sulam, Chong You, 2020, J. Mach. Learn. Res..

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

Michael Elad, Yaniv Romano, Jeremias Sulam, 2018, IEEE Signal Processing Magazine.

Conformal symplectic and relativistic optimization

Daniel P. Robinson, Jeremias Sulam, Ren'e Vidal, 2019, NeurIPS.

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

Ronen Talmon, Yaniv Romano, Jeremias Sulam, 2017, Signal Process..

Deep Learning in Protein Structural Modeling and Design

Jeffrey J. Gray, Wenhao Gao, Jeffrey J. Gray, 2020, Patterns.

Antibody structure prediction using interpretable deep learning

Jeffrey J. Gray, Jeffrey A. Ruffolo, Jeremias Sulam, 2021, bioRxiv.

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

Jeffrey J. Gray, Jeremias Sulam, Sai Pooja Mahajan, 2020, bioRxiv.

Learning to solve TV regularised problems with unrolled algorithms

Jeremias Sulam, Thomas Moreau, Hamza Cherkaoui, 2020, NeurIPS.

MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance

Michael Elad, Yue M. Lu, Yaniv Romano, 2018, IEEE Transactions on Signal Processing.

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

Jeffrey J. Gray, Jeremias Sulam, Jeffrey A. Ruffolo, 2021, ArXiv.

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

Jeremias Sulam, J. Stayman, Jacopo Teneggi, 2023, ArXiv.

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

Michael Elad, Aviad Aberdam, Jeremias Sulam, 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Local Block Coordinate Descent Algorithm for the CSC Model

Michael Elad, Jeremias Sulam, Ev Zisselman, 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Convolutional Dictionary Learning via Local Processing

Michael Elad, Yaniv Romano, Jeremias Sulam, 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

Trainlets: Dictionary Learning in High Dimensions

Michael Elad, Boaz Ophir, Michael Zibulevsky, 2016, IEEE Transactions on Signal Processing.

Automated in vivo Tracking of Cortical Oligodendrocytes

Jeremias Sulam, D. Bergles, Y. Xu, 2021, bioRxiv.

Deep learning prediction of sex on chest radiographs: a potential contributor to biased algorithms

C. Lin, Jeremias Sulam, P. Yi, 2022, Emergency Radiology.

Cross-modality supervised image restoration enables nanoscale tracking of synaptic plasticity in living mice

Adam S. Charles, R. Huganir, Jeremias Sulam, 2022, bioRxiv.

Code and Data Sharing Practices in the Radiology Artificial Intelligence Literature: A Meta-Research Study.

Jeremias Sulam, Samantha M. Santomartino, P. Yi, 2022, Radiology. Artificial intelligence.

Medical imaging data science competitions should report dataset demographics and evaluate for bias

Jeremias Sulam, P. Yi, Vishwa S. Parekh, 2023, Nature Network Boston.

Maximizing AUC with Deep Learning for Classification of Imbalanced Mammogram Datasets

Pavel Kisilev, Jeremias Sulam, Rami Ben-Ari, 2017, VCBM.

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

Michael Elad, Irad Yavneh, Javier Turek, 2015, 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

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

Michael Elad, Yaniv Romano, Aviad Aberdam, 2018, Journal of Mathematical Imaging and Vision.

Tunable neural networks for CT image formation

Jeremias Sulam, G. Gang, Wenying Wang, 2023, Journal of Medical Imaging.

Task-driven CT image quality optimization for low-contrast lesion detectability with tunable neural networks

Jeremias Sulam, K. Boedeker, Tzu-Cheng Lee, 2023, Medical Imaging.

Tunable neural networks for multi-material image formation from spectral CT measurements

Jeremias Sulam, J. Stayman, P. Noel, 2022, Other Conferences.

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

Jeremias Sulam, J. Stayman, Jacopo Teneggi, 2023, ICML.

Understanding Noise-Augmented Training for Randomized Smoothing

Jeremias Sulam, Ambar Pal, 2023, Trans. Mach. Learn. Res..

Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms

Jeremias Sulam, Joshua Agterberg, 2022, AISTATS.

Accurate Prediction of Antimicrobial Susceptibility for Point-of-Care Testing of Urine in Less than 90 Minutes via iPRISM Cassettes.

Jeremias Sulam, E. Segal, S. Halachmi, 2023, Advanced science.

Adversarial robustness of sparse local Lipschitz predictors

Jeremias Sulam, Ramchandran Muthukumar, 2022, SIAM J. Math. Data Sci..

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

Jeremias Sulam, J. Duyn, D. Reich, 2023, NeuroImage.

Visual Transformers and Convolutional Neural Networks for Disease Classification on Radiographs: A Comparison of Performance, Sample Efficiency, and Hidden Stratification.

Jeremias Sulam, Zachary R. Murphy, P. Yi, 2022, Radiology: Artificial Intelligence.

SHAP-XRT: The Shapley Value Meets Conditional Independence Testing

Jeremias Sulam, Yaniv Romano, Jacopo Teneggi, 2022, 2207.07038.

Multi Layer Sparse Coding: the Holistic Way

Michael Elad, Aviad Aberdam, Jeremias Sulam, 2018, SIAM J. Math. Data Sci..

Adversarial Robustness of Supervised Sparse Coding

R. Arora, Jeremias Sulam, Ramchandran Muthumukar, 2020, NeurIPS.

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

Jeremias Sulam, Yaniv Romano, Jacopo Teneggi, 2022, arXiv.org.

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

Jeremias Sulam, Zhenghan Fang, Sam Buchanan, 2023, ICLR.

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

Michael Elad, Jeremias Sulam, Ev Zisselman, 2018, ArXiv.

From Local to Global Sparse Modeling

Michael Elad, Jeremias Sulam, Michael Elad, 2018 .

The use of weakly supervised machine learning for necrosis assessment in patients with osteosarcoma: A pilot study

Jeremias Sulam, A. Levin, Christa L. LiBrizzi, 2023, Journal of orthopaedic research : official publication of the Orthopaedic Research Society.

Improving Pursuit Algorithms Using Stochastic Resonance pdf

Michael Elad, Yue M. Lu, Yaniv Romano, 2018, ArXiv.

Simultaneous multi-transient linear-combination modeling of MRS data improves uncertainty estimation

Jeremias Sulam, D. Simicic, G. Oeltzschner, 2024, bioRxiv.

Large Inpainting of Face Images With Trainlets

Michael Elad, Jeremias Sulam, Michael Elad, 2016, IEEE Signal Processing Letters.

I Bet You Did Not Mean That: Testing Semantic Importance via Betting

Jeremias Sulam, Jacopo Teneggi, 2024, 2405.19146.

Multilayer Convolutional Sparse Modeling: Pursuit and Dictionary Learning

Michael Elad, Yaniv Romano, Jeremias Sulam, 2017, IEEE Transactions on Signal Processing.