Mentornet: Learning data-driven curriculum for very deep neural networks on corrupted labels
Recent deep networks are capable of memorizing the entire data even when the labels are
completely random. To overcome the overfitting on corrupted labels, we propose a novel …
completely random. To overcome the overfitting on corrupted labels, we propose a novel …
Classification with noisy labels by importance reweighting
In this paper, we study a classification problem in which sample labels are randomly
corrupted. In this scenario, there is an unobservable sample with noise-free labels …
corrupted. In this scenario, there is an unobservable sample with noise-free labels …
DC programming and DCA: thirty years of developments
HA Le Thi, T Pham Dinh - Mathematical Programming, 2018 - Springer
The year 2015 marks the 30th birthday of DC (Difference of Convex functions) programming
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …
and DCA (DC Algorithms) which constitute the backbone of nonconvex programming and …
Neural granger causality
While most classical approaches to Granger causality detection assume linear dynamics,
many interactions in real-world applications, like neuroscience and genomics, are inherently …
many interactions in real-world applications, like neuroscience and genomics, are inherently …
Kronecker-basis-representation based tensor sparsity and its applications to tensor recovery
As a promising way for analyzing data, sparse modeling has achieved great success
throughout science and engineering. It is well known that the sparsity/low-rank of a …
throughout science and engineering. It is well known that the sparsity/low-rank of a …
Global convergence of splitting methods for nonconvex composite optimization
We consider the problem of minimizing the sum of a smooth function h with a bounded
Hessian and a nonsmooth function. We assume that the latter function is a composition of a …
Hessian and a nonsmooth function. We assume that the latter function is a composition of a …
Multispectral images denoising by intrinsic tensor sparsity regularization
Multispectral images (MSI) can help deliver more faithful representation for real scenes than
the traditional image system, and enhance the performance of many computer vision tasks …
the traditional image system, and enhance the performance of many computer vision tasks …
Convex optimization algorithms in medical image reconstruction—in the age of AI
The past decade has seen the rapid growth of model based image reconstruction (MBIR)
algorithms, which are often applications or adaptations of convex optimization algorithms …
algorithms, which are often applications or adaptations of convex optimization algorithms …
Open issues and recent advances in DC programming and DCA
HA Le Thi, T Pham Dinh - Journal of Global Optimization, 2024 - Springer
DC (difference of convex functions) programming and DC algorithm (DCA) are powerful
tools for nonsmooth nonconvex optimization. This field was created in 1985 by Pham Dinh …
tools for nonsmooth nonconvex optimization. This field was created in 1985 by Pham Dinh …
Nonconvex-sparsity and nonlocal-smoothness-based blind hyperspectral unmixing
Blind hyperspectral unmixing (HU), as a crucial technique for hyperspectral data
exploitation, aims to decompose mixed pixels into a collection of constituent materials …
exploitation, aims to decompose mixed pixels into a collection of constituent materials …