Yuxin Liu
6 papers in the PaperMetrix corpus
Papers by this author
-
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property
2022 · 2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS)
In the age of big data, the demand for hidden information mining in technological intellectual property (tech-IP) is increasing in discrete countries. Definitely, a considerable number of graph learning algorithms for technological intellectual property have …
-
Dynamic Tracking with Fuzzy Rules for Evolutionary Dynamic Constrained Optimization
2023
Nature-inspired population-based stochastic search algorithms (SSA) have demonstrated effectiveness in solving many real-world dynamic optimization problems (DOPs), such as dynamic optimal power flow (DOPF) problems. The basic idea of solving DOPs using SSAs is to …
-
Wireless Channel Estimation and Equalization Based on Deep Learning
2024
Nowadays, the research on wireless communication is hot, and it has achieved leapfrog development in the past few decades and has made astonishing breakthroughs in emerging applications such as 5G technology, Internet of Things (lOT), …
-
Gearbox fault diagnosis based on Gramian angular field and TLCA-MobileNetV3 with limited samples
2024 · International Journal of Metrology and Quality Engineering
Gearbox fault diagnosis based on traditional deep learning often needs a large number of samples. However, the gearbox fault samples are limited in practical engineering, which could lead to poor diagnosis performance. Based on the …
-
NeuralBeta: Estimating Beta Using Deep Learning
2024 · arXiv (Cornell University)
Traditional approaches to estimating beta in finance often involve rigid assumptions and fail to adequately capture beta dynamics, limiting their effectiveness in use cases like hedging. To address these limitations, we have developed a novel …
-
EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization
2025 · arXiv (Cornell University)
Generating challenging instances is crucial for the evaluation and advancement of combinatorial optimization solvers. In this work, we introduce EALG (Evolutionary Adversarial Generation of Language Model-Guided Generators), a novel framework that automates the co-evolution of …