Search Results for author:
Found 10 papers, 0 papers with code
ColorVein: Colorful Cancelable Vein Biometrics
Unlike previous cancelable template generation schemes, ColorVein does not destroy the original biometric features and introduces additional color information to grayscale vein images.
Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum
Employing DFL methods to solve such general optimization problems leads to the formulation of Decentralized Nonconvex Composite Federated Learning (DNCFL), a topic that remains largely underexplored.
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data
To overcome these limitations, we propose a novel composite federated learning algorithm called \textbf{FedCanon}, designed to solve the optimization problems comprising a possibly non-convex loss function and a weakly convex, potentially non-smooth regularization term.
Improving Fast Adversarial Training via Self-Knowledge Guidance
Based on the analysis, we mainly attribute the observed misalignment and disparity to the imbalanced optimization in FAT, which motivates us to optimize different training data adaptively to enhance robustness.
Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective
However, FAT suffers from catastrophic overfitting, which leads to a performance drop compared with multi-step adversarial training.
A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization
In this paper, we propose a communication-efficient method MG-Skip with probabilistic local updates and multi-gossip communications for decentralized composite (smooth + nonsmooth) optimization, whose stepsize is independent of the number of local updates and the network topology.
A data-driven rutting depth short-time prediction model with metaheuristic optimization for asphalt pavements based on RIOHTrack
Rutting of asphalt pavements is a crucial design criterion in various pavement design guides.
Decentralized Inexact Proximal Gradient Method With Network-Independent Stepsizes for Convex Composite Optimization
The proposed algorithm uses uncoordinated network-independent constant stepsizes and only needs to approximately solve a sequence of proximal mappings, which is advantageous for solving decentralized composite optimization problems where the proximal mappings of the nonsmooth loss functions may not have analytical solutions.
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization
In this paper, we propose a novel primal-dual proximal splitting algorithm (PD-PSA), named BALPA, for the composite optimization problem with equality constraints, where the loss function consists of a smooth term and a nonsmooth term composed with a linear mapping.
DISA: A Dual Inexact Splitting Algorithm for Distributed Convex Composite Optimization
In this paper, we propose a novel Dual Inexact Splitting Algorithm (DISA) for distributed convex composite optimization problems, where the local loss function consists of a smooth term and a possibly nonsmooth term composed with a linear mapping.
