7 papers
Reducing Bias and Variance: Generative Semantic Guidance and Bi-Layer Ensemble for Image Clustering
Feijiang Li, Zhenxiong Li, Jieting Wang +3
Image clustering aims to partition unlabeled image datasets into distinct groups. A core aspect of this task is constructing and leveraging prior knowledge to guide the clustering…
Beyond MSE: Ordinal Cross-Entropy for Probabilistic Time Series Forecasting
Jieting Wang, Huimei Shi, Feijiang Li +1
Time series forecasting is an important task that involves analyzing temporal dependencies and underlying patterns (such as trends, cyclicality, and seasonality) in historical data…
RI-Loss: A Learnable Residual-Informed Loss for Time Series Forecasting
Jieting Wang, Xiaolei Shang, Feijiang Li +1
Time series forecasting relies on predicting future values from historical data, yet most state-of-the-art approaches-including transformer and multilayer perceptron-based models-o…
Ranked Set Sampling-Based Multilayer Perceptron: Improving Generalization via Variance-Based Bounds
Feijiang Li, Liuya Zhang, Jieting Wang +2
Multilayer perceptron (MLP), one of the most fundamental neural networks, is extensively utilized for classification and regression tasks. In this paper, we establish a new general…
k-HyperEdge Medoids for Clustering Ensemble
Feijiang Li, Jieting Wang, Liuya zhang +4
Clustering ensemble has been a popular research topic in data science due to its ability to improve the robustness of the single clustering method. Many clustering ensemble methods…
Hybrid Causal Identification and Causal Mechanism Clustering
Saixiong Liu, Yuhua Qian, Jue Li +2
Bivariate causal direction identification is a fundamental and vital problem in the causal inference field. Among binary causal methods, most methods based on additive noise only u…