Papers global-optimization
“global-optimization” 태그가 달린 논문 658편 · 필터 해제
A Novel Hybrid Grey Wolf Differential Evolution Algorithm
Grey wolf optimizer (GWO) is a nature-inspired stochastic meta-heuristic of the swarm intelligence field that mimics the hunting behavior of grey wolves. Differential evolution (DE) is a popular stochastic algorithm of t…
global-optimizationCA-I2P: Channel-Adaptive Registration Network with Global Optimal Selection
Detection-free methods typically follow a coarse-to-fine pipeline, extracting image and point cloud features for patch-level matching and refining dense pixel-to-point correspondences. However, differences in feature cha…
global-optimizationImage to Point Cloud RegistrationPoint Cloud RegistrationNeRF-based CBCT Reconstruction needs Normalization and Initialization
Cone Beam Computed Tomography (CBCT) is widely used in medical imaging. However, the limited number and intensity of X-ray projections make reconstruction an ill-posed problem with severe artifacts. NeRF-based methods ha…
global-optimizationNeRFFocusing on Tracks for Online Multi-Object Tracking
Multi-object tracking (MOT) is a critical task in computer vision, requiring the accurate identification and continuous tracking of multiple objects across video frames. However, current state-of-the-art methods mainly r…
global-optimizationMulti-Object TrackingObject DetectionObject Tracking+1Load-Aware Training Scheduling for Model Circulation-based Decentralized Federated Learning
This paper proposes Load-aware Tram-FL, an extension of Tram-FL that introduces a training scheduling mechanism to minimize total training time in decentralized federated learning by accounting for both computational and…
Federated Learningglobal-optimizationSchedulingProbability-One Optimization of Generalized Rayleigh Quotient Sum For Multi-Source Generalized Total Least-Squares
This paper addresses the global optimization of the sum of the Rayleigh quotient and the generalized Rayleigh quotient on the unit sphere. While various methods have been proposed for this problem, they do not guarantee …
global-optimizationGPU-accelerated Modeling of Biological Regulatory Networks
The complex regulatory dynamics of a biological network can be succinctly captured using discrete logic models. Given even sparse time-course data from the system of interest, previous work has shown that global optimiza…
CPUglobal-optimizationGPUA Hybrid GA LLM Framework for Structured Task Optimization
GA LLM is a hybrid framework that combines Genetic Algorithms with Large Language Models to handle structured generation tasks under strict constraints. Each output, such as a plan or report, is treated as a gene, and ev…
global-optimizationLanguage ModelingLanguage ModellingRandom-key genetic algorithms: Principles and applications
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as a vector of N random keys, where a random key is a real number randomly generated in the c…
global-optimizationGlobal optimization of graph acquisition functions for neural architecture search
Graph Bayesian optimization (BO) has shown potential as a powerful and data-efficient tool for neural architecture search (NAS). Most existing graph BO works focus on developing graph surrogates models, i.e., metrics of …
Bayesian Optimizationglobal-optimizationNeural Architecture SearchA Divide-and-Conquer Approach for Global Orientation of Non-Watertight Scene-Level Point Clouds Using 0-1 Integer Optimization
Orienting point clouds is a fundamental problem in computer graphics and 3D vision, with applications in reconstruction, segmentation, and analysis. While significant progress has been made, existing approaches mainly fo…
global-optimizationSurface ReconstructionImproving LLM-based Global Optimization with Search Space Partitioning
Large Language Models (LLMs) have recently emerged as effective surrogate models and candidate generators within global optimization frameworks for expensive blackbox functions. Despite promising results, LLM-based metho…
Bayesian Optimizationglobal-optimizationSingle Snapshot Distillation for Phase Coded Mask Design in Phase Retrieval
Phase retrieval (PR) reconstructs phase information from magnitude measurements, known as coded diffraction patterns (CDPs), whose quality depends on the number of snapshots captured using coded phase masks. High-quality…
global-optimizationKnowledge DistillationRetrievalThermoONet -- a deep learning-based small body thermophysical network: applications to modelling water activity of comets
Cometary activity is a compelling subject of study, with thermophysical models playing a pivotal role in its understanding. However, traditional numerical solutions for small body thermophysical models are computationall…
global-optimizationLarge-Scale Gaussian Splatting SLAM
The recently developed Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have shown encouraging and impressive results for visual SLAM. However, most representative methods require RGBD sensors and are only …
3DGSglobal-optimizationNeRFA Generative Neural Annealer for Black-Box Combinatorial Optimization
We propose a generative, end-to-end solver for black-box combinatorial optimization that emphasizes both sample efficiency and solution quality on NP problems. Drawing inspiration from annealing-based algorithms, we trea…
Combinatorial OptimizationData Augmentationglobal-optimizationTime-Modulated EM Skins for Integrated Sensing and Communications
An innovative solution, based on the exploitation of the harmonic beams generated by time-modulated electromagnetic skins (TM-EMSs), is proposed for the implementation of integrated sensing and communication (ISAC) funct…
global-optimizationIntegrated sensing and communicationISACDiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion
Current Structure-from-Motion (SfM) methods typically follow a two-stage pipeline, combining learned or geometric pairwise reasoning with a subsequent global optimization step. In contrast, we propose a data-driven multi…
Denoisingglobal-optimizationA New Scope and Domain Measure Comparison Method for Global Convergence Analysis in Evolutionary Computation
Convergence analysis is a fundamental research topic in evolutionary computation (EC). The commonly used analysis method models the EC algorithm as a homogeneous Markov chain for analysis, which is not always suitable fo…
global-optimizationA Graphical Global Optimization Framework for Parameter Estimation of Statistical Models with Nonconvex Regularization Functions
Optimization problems with norm-bounding constraints arise in a variety of applications, including portfolio optimization, machine learning, and feature selection. A common approach to these problems involves relaxing th…
feature selectionglobal-optimizationparameter estimationPortfolio Optimization