A general hypergraph learning algorithm for drug multi-task predictions in micro-to-macro biomedical networksdoi:10.1371/journal.pcbi.1011597MACHINE learningDEEP learningDRUG discoveryMOLECULAR interactionsFUNC
For hypergraph clustering, various methods have been proposed to define hypergraph p-Laplacians in the literature. This work proposes a general framework f
Martin1887/oxigen - Fast, parallel, extensible and adaptable genetic algorithm library. A example using this library solves the N Queens problem for N = 255 in only few seconds and using less than 1 MB of RAM. pkalivas/radiate - A customizable parallel genetic programming engine capable of ...
Haveliwala TH (2003) Topic-sensitive PageRank: a context-sensitive ranking algorithm for Web search. IEEE Trans Knowl Data Eng 15(4):784–796 MATH Google Scholar Hayat MK, Xue S, Yang J (2023) Self-supervised heterogeneous hypergraph learning with context-aware pooling for graph-level classi...
This branch is25 commits behindgzcsudo/Awesome-Hypergraph-Network:main. Repository files navigation README Hypergraph Survey Hypergraph Learning: Methods and Practices (TPAMI, 2022) [paper] More Recent Advances in (Hyper)Graph Partitioning (ACM Computing Surveys, 2022) [paper] ...
A personalized, federated learning algorithm for stress-level classification was proposed by Jiang et al. In 2023. They demonstrated accuracy improvement by over 7% and 12%, respectively, compared to two recently introduced baseline algorithms under the proposed approach. Also, the two baseline algorit...
Manual review:We conducted a manual full-text examination of the articles resulting from the preliminary screening phase, and retained those that: (1) Focus on methods both for analyzing and/or detecting misinformation,andfor detecting emotions and/or sentiment. (2) Apply learning methods to the ...
follow the observation on the other datasets, whose best performance is achieved when the number of semantic and topology positives are both small. It is because the attributes on Aminer are generated by DeepWalk, a random walk-based algorithm that is biased to hub nodes in the learning ...
A general map matching algorithm for transport telematics applications Map-matching in complex urban road networks Fast map matching, an algorithm integrating hidden markov model with precomputation Deep Learning Methods Deepmm: Deep learning based map matching with data augmentation Transformer-based map...
Adagrad:An adaptive algorithm adjusting learning rates based on parameter update frequency. Effective for sparse data, it can lead to quick convergence but may also converge prematurely and face challenges with non-convex optimization. The choice of optimization algorithm depends on dataset size, model...