Search Results for author: Jeremias Sulam

Found 22 papers, 10 papers with code

Adversarial robustness of sparse local Lipschitz predictors

no code implementations26 Feb 2022 Ramchandran Muthukumar, Jeremias Sulam

This work studies the adversarial robustness of parametric functions composed of a linear predictor and a non-linear representation map.

Adversarial Robustness

Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms

no code implementations8 Feb 2022 Joshua Agterberg, Jeremias Sulam

Sparse Principal Component Analysis (PCA) is a prevalent tool across a plethora of subfields of applied statistics.

Deciphering antibody affinity maturation with language models and weakly supervised learning

no code implementations14 Dec 2021 Jeffrey A. Ruffolo, Jeffrey J. Gray, Jeremias Sulam

Understanding the composition of an individual's immune repertoire can provide insights into this process and reveal potential therapeutic antibodies.

Language Modelling Multiple Instance Learning

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

1 code implementation22 Sep 2021 Zhenzhen Wang, Carla Saoud, Sintawat Wangsiricharoen, Aaron W. James, Aleksander S. Popel, Jeremias Sulam

Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development.

Deep Attention Multiple Instance Learning +1

A Geometric Analysis of Neural Collapse with Unconstrained Features

1 code implementation NeurIPS 2021 Zhihui Zhu, Tianyu Ding, Jinxin Zhou, Xiao Li, Chong You, Jeremias Sulam, Qing Qu

In contrast to existing landscape analysis for deep neural networks which is often disconnected from practice, our analysis of the simplified model not only does it explain what kind of features are learned in the last layer, but it also shows why they can be efficiently optimized in the simplified settings, matching the empirical observations in practical deep network architectures.

Fast Hierarchical Games for Image Explanations

1 code implementation13 Apr 2021 Jacopo Teneggi, Alexandre Luster, Jeremias Sulam

As modern complex neural networks keep breaking records and solving harder problems, their predictions also become less and less intelligible.

Computer Vision Image Classification +1

Learning to solve TV regularised problems with unrolled algorithms

1 code implementation NeurIPS 2020 Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau

In this paper, we accelerate such iterative algorithms by unfolding proximal gradient descent solvers in order to learn their parameters for 1D TV regularized problems.

Learning to solve TV regularized problems with unrolled algorithms

no code implementations19 Oct 2020 Hamza Cherkaoui, Jeremias Sulam, Thomas Moreau

In this paper, we accelerate such iterative algorithms by unfolding proximal gradient descent solvers in order to learn their parameters for 1D TV regularized problems.

Learned Proximal Networks for Quantitative Susceptibility Mapping

1 code implementation11 Aug 2020 Kuo-Wei Lai, Manisha Aggarwal, Peter van Zijl, Xu Li, Jeremias Sulam

More importantly, this framework is believed to be the first deep learning QSM approach that can naturally handle an arbitrary number of phase input measurements without the need for any ad-hoc rotation or re-training.

Image Reconstruction

Deep Learning in Protein Structural Modeling and Design

no code implementations16 Jul 2020 Wenhao Gao, Sai Pooja Mahajan, Jeremias Sulam, Jeffrey J. Gray

Deep learning is catalyzing a scientific revolution fueled by big data, accessible toolkits, and powerful computational resources, impacting many fields including protein structural modeling.

Recovery and Generalization in Over-Realized Dictionary Learning

no code implementations11 Jun 2020 Jeremias Sulam, Chong You, Zhihui Zhu

We thoroughly demonstrate this observation in practice and provide an analysis of this phenomenon by tying recovery measures to generalization bounds.

Dictionary Learning Generalization Bounds

Conformal Symplectic and Relativistic Optimization

1 code implementation NeurIPS 2020 Guilherme França, Jeremias Sulam, Daniel P. Robinson, René Vidal

Arguably, the two most popular accelerated or momentum-based optimization methods in machine learning are Nesterov's accelerated gradient and Polyaks's heavy ball, both corresponding to different discretizations of a particular second order differential equation with friction.

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

2 code implementations1 Nov 2018 Ev Zisselman, Jeremias Sulam, Michael Elad

The Convolutional Sparse Coding (CSC) model has recently gained considerable traction in the signal and image processing communities.

Image Inpainting online learning

MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance

no code implementations26 Jun 2018 Dror Simon, Jeremias Sulam, Yaniv Romano, Yue M. Lu, Michael Elad

The proposed method adds controlled noise to the input and estimates a sparse representation from the perturbed signal.

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

2 code implementations2 Jun 2018 Jeremias Sulam, Aviad Aberdam, Amir Beck, Michael Elad

Parsimonious representations are ubiquitous in modeling and processing information.

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

no code implementations29 May 2018 Yaniv Romano, Aviad Aberdam, Jeremias Sulam, Michael Elad

Despite their impressive performance, deep convolutional neural networks (CNNs) have been shown to be sensitive to small adversarial perturbations.

General Classification

Multi-Layer Sparse Coding: The Holistic Way

no code implementations25 Apr 2018 Aviad Aberdam, Jeremias Sulam, Michael Elad

The recently proposed multi-layer sparse model has raised insightful connections between sparse representations and convolutional neural networks (CNN).

Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning

no code implementations29 Aug 2017 Jeremias Sulam, Vardan Papyan, Yaniv Romano, Michael Elad

We show that the training of the filters is essential to allow for non-trivial signals in the model, and we derive an online algorithm to learn the dictionaries from real data, effectively resulting in cascaded sparse convolutional layers.

Dictionary Learning

Convolutional Dictionary Learning via Local Processing

1 code implementation ICCV 2017 Vardan Papyan, Yaniv Romano, Jeremias Sulam, Michael Elad

Convolutional Sparse Coding (CSC) is an increasingly popular model in the signal and image processing communities, tackling some of the limitations of traditional patch-based sparse representations.

Dictionary Learning Image Inpainting

Trainlets: Dictionary Learning in High Dimensions

no code implementations31 Jan 2016 Jeremias Sulam, Boaz Ophir, Michael Zibulevsky, Michael Elad

Sparse representations has shown to be a very powerful model for real world signals, and has enabled the development of applications with notable performance.

Dictionary Learning

Cannot find the paper you are looking for? You can Submit a new open access paper.