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Found 6 papers, 1 papers with code
The Ideal Continual Learner: An Agent That Never Forgets
We show that ICL unifies multiple well-established continual learning methods and gives new theoretical insights into the strengths and weaknesses of these methods.
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension
Robust subspace recovery (RSR) is a fundamental problem in robust representation learning.
Block-Term Tensor Decomposition Model Selection and Computation: The Bayesian Way
The so-called block-term decomposition (BTD) tensor model, especially in its rank-$(L_r, L_r, 1)$ version, has been recently receiving increasing attention due to its enhanced ability of representing systems and signals that are composed of \emph{blocks} of rank higher than one, a scenario encountered in numerous and diverse applications.
Alternating Iteratively Reweighted Minimization Algorithms for Low-Rank Matrix Factorization
Nowadays, the availability of large-scale data in disparate application domains urges the deployment of sophisticated tools for extracting valuable knowledge out of this huge bulk of information.
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images
Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process.
Online Low-Rank Subspace Learning from Incomplete Data: A Bayesian View
Extracting the underlying low-dimensional space where high-dimensional signals often reside has long been at the center of numerous algorithms in the signal processing and machine learning literature during the past few decades.
