Jeremias Sulam | DeepAI

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∙ 07/01/2023

Sparsity-aware generalization theory for deep neural networks

Deep artificial neural networks achieve surprising generalization abilit...

0 Ramchandran Muthukumar, et al. ∙

∙ 05/08/2023

Understanding Noise-Augmented Training for Randomized Smoothing

Randomized smoothing is a technique for providing provable robustness gu...

0 Ambar Pal, et al. ∙

∙ 02/07/2023

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

Score-based generative modeling, informally referred to as diffusion mod...

0 Jacopo Teneggi, et al. ∙

∙ 11/29/2022

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

Modern machine learning pipelines, in particular those based on deep lea...

0 Jacopo Teneggi, et al. ∙

∙ 09/09/2022

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

Susceptibility tensor imaging (STI) is an emerging magnetic resonance im...

8 Zhenghan Fang, et al. ∙

∙ 07/25/2022

Estimating and Controlling for Fairness via Sensitive Attribute Predictors

Although machine learning classifiers have been increasingly used in hig...

0 Beepul Bharti, et al. ∙

∙ 07/14/2022

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

Machine learning models, in particular artificial neural networks, are i...

13 Jacopo Teneggi, et al. ∙

∙ 02/26/2022

Adversarial robustness of sparse local Lipschitz predictors

This work studies the adversarial robustness of parametric functions com...

0 Ramchandran Muthukumar, et al. ∙

∙ 02/08/2022

Entrywise Recovery Guarantees for Sparse PCA via Sparsistent Algorithms

Sparse Principal Component Analysis (PCA) is a prevalent tool across a p...

0 Joshua Agterberg, et al. ∙

∙ 01/19/2022

Prospective Learning: Back to the Future

Research on both natural intelligence (NI) and artificial intelligence (...

8 Joshua T. Vogelstein, et al. ∙

∙ 12/14/2021

Deciphering antibody affinity maturation with language models and weakly supervised learning

In response to pathogens, the adaptive immune system generates specific ...

0 Jeffrey A. Ruffolo, et al. ∙

∙ 09/22/2021

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

Annotating cancerous regions in whole-slide images (WSIs) of pathology s...

16 Zhenzhen Wang, et al. ∙

∙ 05/06/2021

A Geometric Analysis of Neural Collapse with Unconstrained Features

We provide the first global optimization landscape analysis of Neural Co...

18 Zhihui Zhu, et al. ∙

∙ 04/13/2021

Fast Hierarchical Games for Image Explanations

As modern complex neural networks keep breaking records and solving hard...

8 Jacopo Teneggi, et al. ∙

∙ 10/22/2020

Adversarial Robustness of Supervised Sparse Coding

Several recent results provide theoretical insights into the phenomena o...

0 Jeremias Sulam, et al. ∙

∙ 10/19/2020

Learning to solve TV regularized problems with unrolled algorithms

Total Variation (TV) is a popular regularization strategy that promotes ...

3 Hamza Cherkaoui, et al. ∙

∙ 08/11/2020

Learned Proximal Networks for Quantitative Susceptibility Mapping

Quantitative Susceptibility Mapping (QSM) estimates tissue magnetic susc...

0 Kuo-Wei Lai, et al. ∙

∙ 07/16/2020

Deep Learning in Protein Structural Modeling and Design

Deep learning is catalyzing a scientific revolution fueled by big data, ...

0 Wenhao Gao, et al. ∙

∙ 06/11/2020

Recovery and Generalization in Over-Realized Dictionary Learning

In over two decades of research, the field of dictionary learning has ga...

0 Jeremias Sulam, et al. ∙

∙ 03/11/2019

Conformal Symplectic and Relativistic Optimization

Although momentum-based optimization methods have had a remarkable impac...

0 Guilherme França, et al. ∙

∙ 11/01/2018

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

The Convolutional Sparse Coding (CSC) model has recently gained consider...

0 Ev Zisselman, et al. ∙

∙ 06/26/2018

Improving Pursuit Algorithms Using Stochastic Resonance

Sparse Representation Theory is a sub-field of signal processing that ha...

0 Dror Simon, et al. ∙

∙ 06/02/2018

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

Parsimonious representations in data modeling are ubiquitous and central...

0 Jeremias Sulam, et al. ∙

∙ 04/25/2018

Multi Layer Sparse Coding: the Holistic Way

The recently proposed multi-layer sparse model has raised insightful con...

0 Aviad Aberdam, et al. ∙

∙ 08/29/2017

Multi-Layer Convolutional Sparse Modeling: Pursuit and Dictionary Learning

The recently proposed Multi-Layer Convolutional Sparse Coding (ML-CSC) m...

0 Jeremias Sulam, et al. ∙

∙ 05/09/2017

Convolutional Dictionary Learning via Local Processing

Convolutional Sparse Coding (CSC) is an increasingly popular model in th...

0 Vardan Papyan, et al. ∙

∙ 01/31/2016

Trainlets: Dictionary Learning in High Dimensions

Sparse representations has shown to be a very powerful model for real wo...

0 Jeremias Sulam, et al. ∙

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