11 citations · 25 across the 21 of their papers we have counts for
11 papers · 1 filter
Towards Ultra-Low-Power Neuromorphic Speech Enhancement with Spiking-FullSubNet
Xiang Hao, Chenxiang Ma, Qu Yang +2
Speech enhancement is critical for improving speech intelligibility and quality in various audio devices. In recent years, deep learning-based methods have significantly improved s…
HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models
Yu Zhou, Xingyu Wu, Jibin Wu +2
Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability. It has gained popularity in…
Towards Automated Knowledge Transfer in Evolutionary Multitasking via Large Language Models
Xuebin Lyu, Yuxiao Huang, XueFeng Chen +3
Evolutionary multi-task optimization (EMTO) is an advanced optimization paradigm that improves search efficiency by enabling knowledge transfer across multiple tasks solved in para…
PMSN: A Parallel Multi-compartment Spiking Neuron for Multi-scale Temporal Processing
Xinyi Chen, Jibin Wu, Chenxiang Ma +4
Spiking Neural Networks (SNNs) hold great potential to realize brain-inspired, energy-efficient computational systems. However, current SNNs still fall short in terms of multiscale…
Autonomous Multi-Objective Optimization Using Large Language Model
Yuxiao Huang, Shenghao Wu, Wenjie Zhang +3
Multi-objective optimization problems (MOPs) are ubiquitous in real-world applications, presenting a complex challenge of balancing multiple conflicting objectives. Traditional evo…
Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection
Xingyu Wu, Yan Zhong, Jibin Wu +3
In the algorithm selection research, the discussion surrounding algorithm features has been significantly overshadowed by the emphasis on problem features. Although a few empirical…
CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
Yu Zhou, Xingyu Wu, Jibin Wu +2
The ability to understand causality significantly impacts the competence of large language models (LLMs) in output explanation and counterfactual reasoning, as causality reveals th…
Exploring the True Potential: Evaluating the Black-box Optimization Capability of Large Language Models
Beichen Huang, Xingyu Wu, Yu Zhou +4
Large language models (LLMs) have demonstrated exceptional performance not only in natural language processing tasks but also in a great variety of non-linguistic domains. In diver…
Scaling Supervised Local Learning with Augmented Auxiliary Networks
Chenxiang Ma, Jibin Wu, Chenyang Si +1
Deep neural networks are typically trained using global error signals that backpropagate (BP) end-to-end, which is not only biologically implausible but also suffers from the updat…
Efficient Online Learning for Networks of Two-Compartment Spiking Neurons
Yujia Yin, Xinyi Chen, Chenxiang Ma +2
The brain-inspired Spiking Neural Networks (SNNs) have garnered considerable research interest due to their superior performance and energy efficiency in processing temporal signal…
Evolutionary Computation in the Era of Large Language Model: Survey and Roadmap
Xingyu Wu, Sheng-hao Wu, Jibin Wu +2
Large language models (LLMs) have not only revolutionized natural language processing but also extended their prowess to various domains, marking a significant stride towards artif…