Generative adversarial networks-based semi-supervised learning for hyperspectral image classification
Classification of hyperspectral image (HSI) is an important research topic in the remote
sensing community. Significant efforts (eg, deep learning) have been concentrated on this …
sensing community. Significant efforts (eg, deep learning) have been concentrated on this …
Descent steps of a relation-aware energy produce heterogeneous graph neural networks
Heterogeneous graph neural networks (GNNs) achieve strong performance on node
classification tasks in a semi-supervised learning setting. However, as in the simpler …
classification tasks in a semi-supervised learning setting. However, as in the simpler …
From hypergraph energy functions to hypergraph neural networks
Hypergraphs are a powerful abstraction for representing higher-order interactions between
entities of interest. To exploit these relationships in making downstream predictions, a …
entities of interest. To exploit these relationships in making downstream predictions, a …
A simple graph-based semi-supervised learning approach for imbalanced classification
J Deng, JG Yu - Pattern Recognition, 2021 - Elsevier
Abstract Graph-based Semi-Supervised Learning (GSSL) methods aim to classify unlabeled
data by learning the graph structure and labeled data jointly. In this work, we propose a …
data by learning the graph structure and labeled data jointly. In this work, we propose a …
Auto-tuning parameter choices in hpc applications using bayesian optimization
High performance computing applications, runtimes, and platforms are becoming more
configurable to enable applications to obtain better performance. As a result, users are …
configurable to enable applications to obtain better performance. As a result, users are …
Zoobp: Belief propagation for heterogeneous networks
Given a heterogeneous network, with nodes of different types-eg, products, users and
sellers from an online recommendation site like Amazon-and labels for a few nodes …
sellers from an online recommendation site like Amazon-and labels for a few nodes …
Label propagation with weak supervision
Semi-supervised learning and weakly supervised learning are important paradigms that aim
to reduce the growing demand for labeled data in current machine learning applications. In …
to reduce the growing demand for labeled data in current machine learning applications. In …
Deep analysis of word sense disambiguation via semi-supervised learning and neural word representations
Abstract Word Sense Disambiguation (WSD) aims to determine the meaning of a word in
context. Different approaches have been proposed in supervised and unsupervised …
context. Different approaches have been proposed in supervised and unsupervised …
Learning with inadequate and incorrect supervision
Practically, we are often in the dilemma that the labeled data at hand are inadequate to train
a reliable classifier, and more seriously, some of these labeled data may be mistakenly …
a reliable classifier, and more seriously, some of these labeled data may be mistakenly …
Graph-based semi-supervised learning for relational networks
L Peel - Proceedings of the 2017 SIAM international conference …, 2017 - SIAM
We address the problem of semi-supervised learning in relational networks, networks in
which nodes are entities and links are the relationships or interactions between them …
which nodes are entities and links are the relationships or interactions between them …