Search Results for author: Miguel R. D. Rodrigues

Found 31 papers, 5 papers with code

Optimization Guarantees of Unfolded ISTA and ADMM Networks With Smooth Soft-Thresholding

no code implementations12 Sep 2023 Shaik Basheeruddin Shah, Pradyumna Pradhan, Wei Pu, Ramunaidu Randhi, Miguel R. D. Rodrigues, Yonina C. Eldar

Hence, we provide conditions, in terms of the network width and the number of training samples, on these unfolded networks for the PL$^*$ condition to hold.

Compressive Sensing

Generalization and Estimation Error Bounds for Model-based Neural Networks

no code implementations19 Apr 2023 Avner Shultzman, Eyar Azar, Miguel R. D. Rodrigues, Yonina C. Eldar

In practice, model-based neural networks exhibit higher generalization capability compared to ReLU neural networks.

Learning Algorithm Generalization Error Bounds via Auxiliary Distributions

no code implementations2 Oct 2022 Gholamali Aminian, Saeed Masiha, Laura Toni, Miguel R. D. Rodrigues

Additionally, we demonstrate how our auxiliary distribution method can be used to derive the upper bounds on excess risk of some learning algorithms in the supervised learning context {\blue and the generalization error under the distribution mismatch scenario in supervised learning algorithms, where the distribution mismatch is modeled as $\alpha$-Jensen-Shannon or $\alpha$-R\'enyi divergence between the distribution of test and training data samples distributions.}

Semi-supervised Batch Learning From Logged Data

no code implementations15 Sep 2022 Gholamali Aminian, Armin Behnamnia, Roberto Vega, Laura Toni, Chengchun Shi, Hamid R. Rabiee, Omar Rivasplata, Miguel R. D. Rodrigues

We propose learning methods for problems where feedback is missing for some samples, so there are samples with feedback and samples missing-feedback in the logged data.


Theoretical Perspectives on Deep Learning Methods in Inverse Problems

no code implementations29 Jun 2022 Jonathan Scarlett, Reinhard Heckel, Miguel R. D. Rodrigues, Paul Hand, Yonina C. Eldar

In recent years, there have been significant advances in the use of deep learning methods in inverse problems such as denoising, compressive sensing, inpainting, and super-resolution.

Compressive Sensing Denoising +1

An Information-theoretical Approach to Semi-supervised Learning under Covariate-shift

no code implementations24 Feb 2022 Gholamali Aminian, Mahed Abroshan, Mohammad Mahdi Khalili, Laura Toni, Miguel R. D. Rodrigues

A common assumption in semi-supervised learning is that the labeled, unlabeled, and test data are drawn from the same distribution.

Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover

no code implementations20 Oct 2021 Wei Pu, Chao Zhou, Yonina C. Eldar, Miguel R. D. Rodrigues

In this paper, we consider deep neural networks for solving inverse problems that are robust to forward model mis-specifications.

Compressive Sensing Rolling Shutter Correction

Blind Pareto Fairness and Subgroup Robustness

no code implementations1 Jan 2021 Natalia Martinez, Martin Bertran, Afroditi Papadaki, Miguel R. D. Rodrigues, Guillermo Sapiro

With the wide adoption of machine learning algorithms across various application domains, there is a growing interest in the fairness properties of such algorithms.


Image Separation with Side Information: A Connected Auto-Encoders Based Approach

no code implementations16 Sep 2020 Wei Pu, Barak Sober, Nathan Daly, Zahra Sabetsarvestani, Catherine Higgitt, Ingrid Daubechies, Miguel R. D. Rodrigues

These features are then used to (1) reproduce both of the original RGB images, (2) reconstruct the hypothetical separated X-ray images, and (3) regenerate the mixed X-ray image.

Model-Aware Regularization For Learning Approaches To Inverse Problems

no code implementations18 Jun 2020 Jaweria Amjad, Zhaoyan Lyu, Miguel R. D. Rodrigues

There are various inverse problems -- including reconstruction problems arising in medical imaging -- where one is often aware of the forward operator that maps variables of interest to the observations.

Lautum Regularization for Semi-supervised Transfer Learning

no code implementations2 Apr 2019 Daniel Jakubovitz, Miguel R. D. Rodrigues, Raja Giryes

We focus on the task of semi-supervised transfer learning, in which unlabeled samples from the target dataset are available during the network training on the source dataset.

Transfer Learning

Deep Learning for Inverse Problems: Bounds and Regularizers

no code implementations31 Jan 2019 Jaweria Amjad, Zhaoyan Lyu, Miguel R. D. Rodrigues

Inverse problems arise in a number of domains such as medical imaging, remote sensing, and many more, relying on the use of advanced signal and image processing approaches -- such as sparsity-driven techniques -- to determine their solution.

Image Super-Resolution

Generalization Error in Deep Learning

no code implementations3 Aug 2018 Daniel Jakubovitz, Raja Giryes, Miguel R. D. Rodrigues

Deep learning models have lately shown great performance in various fields such as computer vision, speech recognition, speech translation, and natural language processing.

speech-recognition Speech Recognition +1

Multi-modal Image Processing based on Coupled Dictionary Learning

no code implementations26 Jun 2018 Pingfan Song, Miguel R. D. Rodrigues

In real-world scenarios, many data processing problems often involve heterogeneous images associated with different imaging modalities.

Denoising Dictionary Learning +1

Coupled Dictionary Learning for Multi-contrast MRI Reconstruction

1 code implementation26 Jun 2018 Pingfan Song, Lior Weizman, Joao F. C. Mota, Yonina C. Eldar, Miguel R. D. Rodrigues

In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage an available guidance contrast to restore the target contrast.

Anatomy Denoising +2

Multimodal Image Denoising based on Coupled Dictionary Learning

no code implementations26 Jun 2018 Pingfan Song, Miguel R. D. Rodrigues

The first stage performs joint sparse transform for multimodal images with respect to a group of learned coupled dictionaries, followed by a shrinkage operation on the sparse representations.

Dictionary Learning Image Denoising

Multimodal Image Super-resolution via Joint Sparse Representations induced by Coupled Dictionaries

1 code implementation25 Sep 2017 Pingfan Song, Xin Deng, João F. C. Mota, Nikos Deligiannis, Pier Luigi Dragotti, Miguel R. D. Rodrigues

This paper proposes a new approach to construct a high-resolution (HR) version of a low-resolution (LR) image given another HR image modality as reference, based on joint sparse representations induced by coupled dictionaries.

Dictionary Learning Image Super-Resolution

Learning to Succeed while Teaching to Fail: Privacy in Closed Machine Learning Systems

no code implementations23 May 2017 Jure Sokolic, Qiang Qiu, Miguel R. D. Rodrigues, Guillermo Sapiro

Confronted with this challenge, in this paper we open a new line of research, where the security, privacy, and fairness is learned and used in a closed environment.

BIG-bench Machine Learning Blocking +1

Generalization Error of Invariant Classifiers

no code implementations14 Oct 2016 Jure Sokolic, Raja Giryes, Guillermo Sapiro, Miguel R. D. Rodrigues

We show that whereas the generalization error of a non-invariant classifier is proportional to the complexity of the input space, the generalization error of an invariant classifier is proportional to the complexity of the base space.

Multi-modal dictionary learning for image separation with application in art investigation

no code implementations14 Jul 2016 Nikos Deligiannis, Joao F. C. Mota, Bruno Cornelis, Miguel R. D. Rodrigues, Ingrid Daubechies

Our dictionary learning framework can be tailored both to a single- and a multi-scale framework, with the latter leading to a significant performance improvement.

Dictionary Learning

Bounds on the Number of Measurements for Reliable Compressive Classification

no code implementations11 Jul 2016 Hugo Reboredo, Francesco Renna, Robert Calderbank, Miguel R. D. Rodrigues

This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements.

Classification General Classification

Robust Large Margin Deep Neural Networks

no code implementations26 May 2016 Jure Sokolic, Raja Giryes, Guillermo Sapiro, Miguel R. D. Rodrigues

The generalization error of deep neural networks via their classification margin is studied in this work.

X-ray image separation via coupled dictionary learning

no code implementations20 May 2016 Nikos Deligiannis, João F. C. Mota, Bruno Cornelis, Miguel R. D. Rodrigues, Ingrid Daubechies

In support of art investigation, we propose a new source sepa- ration method that unmixes a single X-ray scan acquired from double-sided paintings.

Dictionary Learning

Classification and Reconstruction of High-Dimensional Signals from Low-Dimensional Features in the Presence of Side Information

no code implementations1 Dec 2014 Francesco Renna, Liming Wang, Xin Yuan, Jianbo Yang, Galen Reeves, Robert Calderbank, Lawrence Carin, Miguel R. D. Rodrigues

These conditions, which are reminiscent of the well-known Slepian-Wolf and Wyner-Ziv conditions, are a function of the number of linear features extracted from the signal of interest, the number of linear features extracted from the side information signal, and the geometry of these signals and their interplay.

General Classification

Compressed Sensing with Prior Information: Optimal Strategies, Geometry, and Bounds

2 code implementations22 Aug 2014 Joao F. C. Mota, Nikos Deligiannis, Miguel R. D. Rodrigues

Our bounds and geometrical interpretations reveal that if the prior information has good enough quality, L1-L1 minimization improves the performance of CS dramatically.

Information Theory Information Theory

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