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Found 11 papers, 6 papers with code
Nearest-Neighbour-Induced Isolation Similarity and its Impact on Density-Based Clustering
A new type of clusters called mass-connected clusters is formally defined.
Online Semi-Supervised Concept Drift Detection with Density Estimation
The two types of drift that are extensively studied are real drift and virtual drift where the former is the change in posterior probabilities p(y|X) while the latter is the change in distribution of X without affecting the posterior probabilities.
Anomaly Detection in Dynamic Graphs via Transformer
Detecting anomalies for dynamic graphs has drawn increasing attention due to their wide applications in social networks, e-commerce, and cybersecurity.
Deep Learning for Embodied Vision Navigation: A Survey
A navigation agent is supposed to have various intelligent skills, such as visual perceiving, mapping, planning, exploring and reasoning, etc.
Enhancing Digital Health Services: A Machine Learning Approach to Personalized Exercise Goal Setting
This study aims to fill this gap by developing a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior trajectory.
Optimal service resource management strategy for IoT-based health information system considering value co-creation of users
This paper explores optimal service resource management strategy, a continuous challenge for health information service to enhance service performance, optimise service resource utilisation and deliver interactive health information service.
Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
Instead of similarity computation, GGD directly discriminates two groups of node samples with a very simple binary cross-entropy loss.
Unifying Graph Contrastive Learning with Flexible Contextual Scopes
To address aforementioned problems, we present a simple self-supervised learning method termed Unifying Graph Contrastive Learning with Flexible Contextual Scopes (UGCL for short).
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges.
Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
Real-world graphs generally have only one kind of tendency in their connections.
From Data to Insights: A Comprehensive Survey on Advanced Applications in Thyroid Cancer Research
Thyroid cancer, the most prevalent endocrine cancer, has gained significant global attention due to its impact on public health.
