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Found 12 papers, 6 papers with code
LSEnet: Lorentz Structural Entropy Neural Network for Deep Graph Clustering
DSI is also theoretically presented as a new graph clustering objective, not requiring the predefined cluster number.
Semi-Supervised Clustering via Structural Entropy with Different Constraints
In this work, we present Semi-supervised clustering via Structural Entropy (SSE), a novel method that can incorporate different types of constraints from diverse sources to perform both partitioning and hierarchical clustering.
MultiSPANS: A Multi-range Spatial-Temporal Transformer Network for Traffic Forecast via Structural Entropy Optimization
Based on this, we propose a relative structural entropy-based position encoding and a multi-head attention masking scheme based on multi-layer encoding trees.
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel Graphs
In this work, we propose a novel unsupervised Skin Lesion sEgmentation framework based on structural entropy and isolation forest outlier Detection, namely SLED.
Hierarchical State Abstraction Based on Structural Information Principles
State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations.
BCE-Net: Reliable Building Footprints Change Extraction based on Historical Map and Up-to-Date Images using Contrastive Learning
This contrastive learning strategy allowed us to inject the semantics of buildings into a pipeline for the detection of changes, which is achieved by increasing the distinguishability of features of buildings from those of non-buildings.
SE-GSL: A General and Effective Graph Structure Learning Framework through Structural Entropy Optimization
Graph Neural Networks (GNNs) are de facto solutions to structural data learning.
Secure Your Ride: Real-time Matching Success Rate Prediction for Passenger-Driver Pairs
In recent years, online ride-hailing platforms have become an indispensable part of urban transportation.
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graph
Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i. e., weights) of passenger demands between two connected regions.
Agreement-based Learning of Parallel Lexicons and Phrases from Non-Parallel Corpora
We introduce an agreement-based approach to learning parallel lexicons and phrases from non-parallel corpora.
