82 citations · 105 across the 11 of their papers we have counts for
11 papers
Self-Improving is Often Sudden: Enlightenment-style Finetuning for Large-Scale Models
Jing-Xiao Liao, Tianwei Zhang, Yu-Hao Jiang +3
The pursuit of autonomously self-improving models has attracted growing interest in the era of large-scale foundation models. Drawing inspiration from the concept of "enlightenment…
NeuronSeek: On Stability and Expressivity of Task-driven Neurons
Hanyu Pei, Jing-Xiao Liao, Qibin Zhao +4
Drawing inspiration from our human brain that designs different neurons for different tasks, recent advances in deep learning have explored modifying a network's neurons to develop…
Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise
Jing-Xiao Liao, Chao He, Jipu Li +3
Blind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise. Despi…
EEG-DG: A Multi-Source Domain Generalization Framework for Motor Imagery EEG Classification
Xiao-Cong Zhong, Qisong Wang, Dan Liu +5
Motor imagery EEG classification plays a crucial role in non-invasive Brain-Computer Interface (BCI) research. However, the classification is affected by the non-stationarity and i…
A class-weighted supervised contrastive learning long-tailed bearing fault diagnosis approach using quadratic neural network
Wei-En Yu, Jinwei Sun, Shiping Zhang +2
Deep learning has achieved remarkable success in bearing fault diagnosis. However, its performance oftentimes deteriorates when dealing with highly imbalanced or long-tailed data,…
Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise
Hang-Cheng Dong, Yuhao Jiang, Yingyan Huang +4
Class activation maps are widely used for explaining deep neural networks. Due to its ability to highlight regions of interest, it has evolved in recent years as a key step in weak…
BearingPGA-Net: A Lightweight and Deployable Bearing Fault Diagnosis Network via Decoupled Knowledge Distillation and FPGA Acceleration
Jing-Xiao Liao, Sheng-Lai Wei, Chen-Long Xie +5
Deep learning has achieved remarkable success in the field of bearing fault diagnosis. However, this success comes with larger models and more complex computations, which cannot be…
One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks
Feng-Lei Fan, Hang-Cheng Dong, Zhongming Wu +4
Inspired by neuronal diversity in the biological neural system, a plethora of studies proposed to design novel types of artificial neurons and introduce neuronal diversity into art…
Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis
Jing-Xiao Liao, Hang-Cheng Dong, Zhi-Qi Sun +3
Bearing fault diagnosis is of great importance to decrease the damage risk of rotating machines and further improve economic profits. Recently, machine learning, represented by dee…
Quadratic Neuron-empowered Heterogeneous Autoencoder for Unsupervised Anomaly Detection
Jing-Xiao Liao, Bo-Jian Hou, Hang-Cheng Dong +5
Inspired by the complexity and diversity of biological neurons, a quadratic neuron is proposed to replace the inner product in the current neuron with a simplified quadratic functi…
Training Neural Networks for Solving 1-D Optimal Piecewise Linear Approximation
Hangcheng Dong, Jingxiao Liao, Yan Wang +4
Recently, the interpretability of deep learning has attracted a lot of attention. A plethora of methods have attempted to explain neural networks by feature visualization, saliency…