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Found 17 papers, 5 papers with code
Unrolled Variational Bayesian Algorithm for Image Blind Deconvolution
In this paper, we introduce a variational Bayesian algorithm (VBA) for image blind deconvolution.
Deep Transform and Metric Learning Networks
Based on its great successes in inference and denosing tasks, Dictionary Learning (DL) and its related sparse optimization formulations have garnered a lot of research interest.
DeConFuse : A Deep Convolutional Transform based Unsupervised Fusion Framework
This work proposes an unsupervised fusion framework based on deep convolutional transform learning.
SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems
This work proposes a supervised multi-channel time-series learning framework for financial stock trading.
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis
This work addresses the problem of analyzing multi-channel time series data %.
Deep Convolutional Transform Learning -- Extended version
This work introduces a new unsupervised representation learning technique called Deep Convolutional Transform Learning (DCTL).
DeepVir -- Graphical Deep Matrix Factorization for "In Silico" Antiviral Repositioning: Application to COVID-19
This work formulates antiviral repositioning as a matrix completion problem where the antiviral drugs are along the rows and the viruses along the columns.
A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials
The main contribution of this work is a manually curated database publicly shared, comprising of existing associations between viruses and their corresponding antivirals.
Quantitative Methods
Deep Transform and Metric Learning Network: Wedding Deep Dictionary Learning and Neural Networks
On account of its many successes in inference tasks and denoising applications, Dictionary Learning (DL) and its related sparse optimization problems have garnered a lot of research interest.
Deep Latent Factor Model for Collaborative Filtering
Latent factor models have been used widely in collaborative filtering based recommender systems.
Transformed Subspace Clustering
We assume that, even if the raw data is not separable into subspac-es, one can learn a representation (transform coef-ficients) such that the learnt representation is sep-arable into subspaces.
General risk measures for robust machine learning
A wide array of machine learning problems are formulated as the minimization of the expectation of a convex loss function on some parameter space.
Deep Unfolding of a Proximal Interior Point Method for Image Restoration
Variational methods are widely applied to ill-posed inverse problems for they have the ability to embed prior knowledge about the solution.
A Two-Stage Subspace Trust Region Approach for Deep Neural Network Training
In this paper, we develop a novel second-order method for training feed-forward neural nets.
A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression
In this paper, we propose a new optimization algorithm for sparse logistic regression based on a stochastic version of the Douglas-Rachford splitting method.
A Fast Algorithm Based on a Sylvester-like Equation for LS Regression with GMRF Prior
This paper presents a fast approach for penalized least squares (LS) regression problems using a 2D Gaussian Markov random field (GMRF) prior.
A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise
We demonstrate the potential of the proposed approach through comparisons with state-of-the-art techniques that are specifically tailored to signal recovery in the presence of mixed Poisson-Gaussian noise.
