5 citations · 6 across the 7 of their papers we have counts for
8 papers
LLN: Learnable Lens Networks for Parameter-Efficient Long-Horizon Dynamical Prediction
Binbin Yong, Zhao Su, Lan Guo +3
Explicit residual connections of the form (x+f(x)), often combined with normalization layers, have become a standard strategy for training very deep neural networks. However, resid…
Fuzzy-MoE: Interpretable Regime-Conditioned Expert Routing for Non-Stationary Multivariate Time Series Forecasting
Lan Guo, Jie Xiao, Zhao Su +5
In non-stationary multivariate time series, different variables and samples often exhibit heterogeneous latent dynamic states, while existing deep forecasting models usually compre…
Core-KAN: Continuous Vision Kernels with Kolmogorov-Arnold Networks
Lan Guo, Mengling Li, Haoran Li +4
Conventional convolutional kernels are typically defined on fixed discrete grids, limiting their ability to accommodate heterogeneous local structures. Existing adaptive operators…
HYDRA: Hyperbolic Dynamic Representation Architecture for Kolmogorov-Arnold Networks
Zhao Su, Yuxin Xia, Haoran Li +4
Kolmogorov-Arnold Networks (KANs) enhance nonlinear function approximation by replacing scalar weights with learnable univariate functions. However, assigning an independent functi…
HyperANFIS: Enhancing Rule Representation and Interpretability in Adaptive Neuro-Fuzzy Systems via Hyperbolic Geometry
Haoran Pei, Zhao Su, Zetao Lin +6
The adaptive neuro-fuzzy inference system (ANFIS) is an interpretable reasoning framework capable of generating explicit IF-THEN fuzzy rules, making it suitable for tasks requiring…
KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning
Binbin Yong, Haoran Pei, Jun Shen +3
Adaptive Neuro-Fuzzy Inference System (ANFIS) was designed to combine the learning capabilities of neural network with the reasoning transparency of fuzzy logic. However, conventio…
DST-GTN: Dynamic Spatio-Temporal Graph Transformer Network for Traffic Forecasting
Songtao Huang, Hongjin Song, Tianqi Jiang +5
Accurate traffic forecasting is essential for effective urban planning and congestion management. Deep learning (DL) approaches have gained colossal success in traffic forecasting…
Crop and weed classification based on AutoML
Xuetao Jiang, Binbin Yong, Soheila Garshasbi +3
CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature. However, to manually choose and fine-tune…