It has potential: Gradient-driven denoisers for convergent solutions to inverse problems
In recent years there has been increasing interest in leveraging denoisers for solving
general inverse problems. Two leading frameworks are regularization-by-denoising (RED) …
general inverse problems. Two leading frameworks are regularization-by-denoising (RED) …
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 …
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 …
DC-programming for neural network optimizations
We discuss two key problems related to learning and optimization of neural networks: the
computation of the adversarial attack for adversarial robustness and approximate …
computation of the adversarial attack for adversarial robustness and approximate …
Over-the-air computation via reconfigurable intelligent surface
Over-the-air computation (AirComp) is a disruptive technique for fast wireless data
aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition …
aggregation in Internet of Things (IoT) networks via exploiting the waveform superposition …
Unsupervised quadratic surface support vector machine with application to credit risk assessment
J Luo, X Yan, Y Tian - European Journal of Operational Research, 2020 - Elsevier
Unsupervised classification is a highly important task of machine learning methods.
Although achieving great success in supervised classification, support vector machine …
Although achieving great success in supervised classification, support vector machine …
Rank-based decomposable losses in machine learning: A survey
Recent works have revealed an essential paradigm in designing loss functions that
differentiate individual losses versus aggregate losses. The individual loss measures the …
differentiate individual losses versus aggregate losses. The individual loss measures the …
The boosted difference of convex functions algorithm for nonsmooth functions
FJ Aragón Artacho, PT Vuong - SIAM Journal on Optimization, 2020 - SIAM
The boosted difference of convex functions algorithm (BDCA) was recently proposed for
minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence …
minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence …
DC Neural Networks avoid overfitting in one-dimensional nonlinear regression
In this paper, we analyze Difference of Convex Neural Networks in the context of one-
dimensional nonlinear regression. Specifically, we show the surprising ability of the …
dimensional nonlinear regression. Specifically, we show the surprising ability of the …