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Found 9 papers, 4 papers with code
A Multi-Layer Regression based Predicable Function Fitting Network
This technique constructs the network includes three main parts: 1) the stationary transform layer, 2) the feature encoding layers, and 3) the fine tuning regression layer.
Spatially and Robustly Hybrid Mixture Regression Model for Inference of Spatial Dependence
Compared with existing spatial regression models, our proposed model assumes the existence a few distinct regression models that are estimated based on observations that exhibit similar response-predictor relationships.
Principled Hyperedge Prediction with Structural Spectral Features and Neural Networks
SNALS captures the joint interactions of a hyperedge by its local environment, which is retrieved by collecting the spectrum information of their connections.
RETHINKING LOCAL LOW RANK MATRIX DETECTION:A MULTIPLE-FILTER BASED NEURAL NETWORK FRAMEWORK
In this work, we first organize the general MLLRR problem into three subproblems based on different low rank properties , and we argue that most of existing efforts focus on only one category, which leaves the other two unsolved.
Geometric All-Way Boolean Tensor Decomposition
Boolean tensor has been broadly utilized in representing high dimensional logical data collected on spatial, temporal and/or other relational domains.
Denoising individual bias for a fairer binary submatrix detection
Low rank representation of binary matrix is powerful in disentangling sparse individual-attribute associations, and has received wide applications.
Supervised clustering of high dimensional data using regularized mixture modeling
Identifying relationships between molecular variations and their clinical presentations has been challenged by the heterogeneous causes of a disease.
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection
Outlier detection (OD) is a key machine learning (ML) task for identifying abnormal objects from general samples with numerous high-stake applications including fraud detection and intrusion detection.
Fast And Efficient Boolean Matrix Factorization By Geometric Segmentation
Boolean matrix factorization (BMF) aims to find an approximation of a binary matrix as the Boolean product of two low rank Boolean matrices, which could generate vast amount of information for the patterns of relationships between the features and samples.
