Results 61 to 70 of about 229,000 (311)
Multi-Target Feature Selection with Adaptive Graph Learning and Target Correlations
MathematicsIn this paper, we present a novel multi-target feature selection algorithm that incorporates adaptive graph learning and target correlations. Specifically, our proposed approach introduces the low-rank constraint on the regression matrix, allowing us to ...
Yujing Zhou, Dubo He
doaj +1 more source
A Graph-based Context Learning Technique for Image Parsing [PDF]
, 2023 The modern deep learning-based architectures have performed well for pixel-wise segmentation tasks. The consideration of context is of vital importance for generation of accurate semantic information. In this research, a deep learning-based image parsing
Azam, Basim, Verma, Brijesh
core +1 more source
2020 IEEE International Conference on Big Data (Big Data), 2020 Features representation leverages the great power in network analysis tasks. However, most features are discrete which poses tremendous challenges to effective use. Recently, increasing attention has been paid on network feature learning, which could map discrete features to continued space.
Ke Sun 0011 +4 more
openaire +3 more sources
Interpreting the effects of DNA polymerase variants at the structural level
Molecular Oncology, EarlyView.Using MAVISp and molecular dynamics simulations, we analyzed over 60 000 missense variants in POLE and POLD1 from ClinVar, COSMIC, cBioPortal, and saturation mutagenesis. Identified mechanistic indicators, including stability, binding, and long‐range, enable structural interpretation, providing ACMG‐like evidence for possible reclassification of VUS ...
Matteo Arnaudi +7 more
wiley +1 more source
Incomplete Multi-View Clustering Based on Dynamic Dimensionality Reduction Weighted Graph Learning
IEEE AccessAiming at the existing incomplete multi-view clustering methods that usually ignore the noise and redundancy of the original data, hide the valuable information in the missing views, and the different importance of each view, this paper proposes the ...
Yaosong Yu, Dongpu Sun
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Interpreting Deep Graph Convolutional Networks with Spectrum Perspective
Mathematics, 2023 Graph convolutional network (GCN) architecture is the basis of many neural networks and has been widely used in processing graph-structured data. When dealing with large and sparse data, deeper GCN models are often required.
Sisi Zhang +3 more
doaj +1 more source
Graph-RAT programming environment [PDF]
, 2009 Graph-RAT is a new programming environment specializing in relational data mining. It incorporates a number of different techniques into a single framework for data collection, data cleaning, propositionalization, and analysis. The language is functional
McEnnis, Daniel
core
Learning Graph Search Heuristics [PDF]
, 2022 Searching for a path between two nodes in a graph is one of the most well-studied and fundamental problems in computer science. In numerous domains such as robotics, AI, or biology, practitioners develop search heuristics to accelerate their pathfinding ...
Lio P. +6 more
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OpenWGL: Open-World Graph Learning [PDF]
, 2020 In traditional graph learning tasks, such as node classification, learning is carried out in a closed-world setting where the number of classes and their training samples are provided to help train models, and the learning goal is to correctly classify ...
Shirui Pan +8 more
core +1 more source
Lifelong Graph Learning for Graph Summarization
2024 IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)Summarizing web graphs is challenging due to the heterogeneity of the modeled information and its changes over time. We investigate the use of neural networks for lifelong graph summarization. Assuming we observe the web graph at a certain time, we train the networks to summarize graph vertices.
Jonatan Frank +4 more
openaire +2 more sources