50 citations · 130 across the 15 of their papers we have counts for
16 papers
Low-Rank Ternary Adaptation for Fine-Tuning Transformers
Alexandru-Dragos Manolache, Yunqiang Li, Jan van Gemert
Ternary transformers offer extreme memory and compute efficiency, but existing low-bit LoRA-based methods cannot directly fine-tune ternary weights. Current approaches either requi…
An adaptive experience-based discrete genetic algorithm for multi-trip picking robot task scheduling in smart orchards
Peng Chen, Jing Liangb, Kang-Jia Qiao +5
The continuous innovation of smart robotic technologies is driving the development of smart orchards, significantly enhancing the potential for automated harvesting systems. While…
GuardFed: A Trustworthy Federated Learning Framework Against Dual-Facet Attacks
Yanli Li, Yanan Zhou, Zhongliang Guo +6
Federated learning (FL) enables privacy-preserving collaborative model training but remains vulnerable to adversarial behaviors that compromise model utility or fairness across sen…
An Integrated Fusion Framework for Ensemble Learning Leveraging Gradient Boosting and Fuzzy Rule-Based Models
Jinbo Li, Peng Liu, Long Chen +2
The integration of different learning paradigms has long been a focus of machine learning research, aimed at overcoming the inherent limitations of individual methods. Fuzzy rule-b…
Negation-Aware Weighted Information Fusion for Reliable Evidential Reasoning
Qiying Hu, Yingying Liang, and Rui Sun +2
Dempster--Shafer theory (DST) provides an attractive framework for multi-source information fusion. However, its effectiveness critically depends on how a BPA is constructed for ea…
Multi-objective task allocation for electric harvesting robots: a hierarchical route reconstruction approach
Peng Chen, Jing Liang, Hui Song +5
The increasing labor costs in agriculture have accelerated the adoption of multi-robot systems for orchard harvesting. However, efficiently coordinating these systems is challengin…
LLM4CMO: Large Language Model-aided Algorithm Design for Constrained Multiobjective Optimization
Zhen-Song Chen, Hong-Wei Ding, Xian-Jia Wang +1
Constrained multi-objective optimization problems (CMOPs) frequently arise in real-world applications where multiple conflicting objectives must be optimized under complex constrai…
ZeroLM: Data-Free Transformer Architecture Search for Language Models
Zhen-Song Chen, Hong-Wei Ding, Xian-Jia Wang +1
Neural architecture search (NAS) provides a systematic framework for automating the design of neural network architectures, yet its widespread adoption is hindered by prohibitive c…
Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls
Can Gao, Xiaofeng Tan, Jie Zhou +2
Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a v…
Cost-Effective Community-Hierarchy-Based Mutual Voting Approach for Influence Maximization in Complex Networks
Yi Liu, Xiaoan Tang, Witold Pedrycz +1
Various types of promising techniques have come into being for influence maximization whose aim is to identify influential nodes in complex networks. In essence, real-world applica…
A Distance Similarity-based Genetic Optimization Algorithm for Satellite Ground Network Planning Considering Feeding Mode
Yingying Ren, Qiuli Li, Yangyang Guo +4
With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the…
An Evolutionary Task Scheduling Algorithm Using Fuzzy Fitness Evaluation Method for Communication Satellite Network
Xuemei Jiang, Yangyang Guo, Yue Zhang +3
Communications satellite network (CSN), as an integral component of the next generation of communication systems, has the capability to offer services globally. Data transmission i…
CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement
Xu Wu, XianXu Hou, Zhihui Lai +4
Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness de…
A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
Saidul Islam, Hanae Elmekki, Ahmed Elsebai +4
Transformer is a deep neural network that employs a self-attention mechanism to comprehend the contextual relationships within sequential data. Unlike conventional neural networks…
TrustGuard: GNN-based Robust and Explainable Trust Evaluation with Dynamicity Support
Jie Wang, Zheng Yan, Jiahe Lan +2
Trust evaluation assesses trust relationships between entities and facilitates decision-making. Machine Learning (ML) shows great potential for trust evaluation owing to its learni…
Mining Large Independent Sets on Massive Graphs
Yu Zhang, Witold Pedrycz, Chanjuan Liu +1
The Maximum Independent Set problem is fundamental for extracting conflict-free structure from large graphs, with applications in scheduling, recommendation, and network analysis.…