Results 1 to 10 of about 115,562 (111)
Holomorphic subgraph reduction of higher-point modular graph forms
Modular graph forms are a class of modular covariant functions which appear in the genus-one contribution to the low-energy expansion of closed string scattering amplitudes.
Jan E. Gerken, Justin Kaidi
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GrSrNMF: dynamic community detection with graph and symmetry bi-regularized non-negative matrix factorization [PDF]
Community detection in dynamic networks has become an interesting and popular research direction in recent years, widely used in electronic commerce, social media, and other fields.
Wei Yu +6 more
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Graph-Enhanced Expectation Maximization for Emission Tomography [PDF]
Emission tomography, including single-photon emission computed tomography (SPECT), requires image reconstruction from noisy and incomplete projection data.
Ryosuke Kasai, Hideki Otsuka
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Node-Adaptive Regularization for Graph Signal Reconstruction
A critical task in graph signal processing is to estimate the true signal from noisy observations over a subset of nodes, also known as the reconstruction problem.
Maosheng Yang +3 more
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Neural Networks Regularization With Graph-Based Local Resampling
This paper presents the concept of Graph-based Local Resampling of perceptron-like neural networks with random projections (RN-ELM) which aims at regularization of the yielded model.
Alex D. Assis +4 more
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Catastrophic Forgetting in Deep Graph Networks: A Graph Classification Benchmark
In this work, we study the phenomenon of catastrophic forgetting in the graph representation learning scenario. The primary objective of the analysis is to understand whether classical continual learning techniques for flat and sequential data have a ...
Antonio Carta +4 more
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Hyperspectral Image Super-Resolution Algorithm Based on Graph Regular Tensor Ring Decomposition
This paper introduces a novel hyperspectral image super-resolution algorithm based on graph-regularized tensor ring decomposition aimed at resolving the challenges of hyperspectral image super-resolution.
Shasha Sun +5 more
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Although a graph-based semisupervised learning (SSL) approach can utilize limited numbers of labeled samples for hyperspectral image (HSI) classification, it is difficult to use the large amount of pixels in an HSI to construct a large-scale graph.
Longshan Yang +4 more
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PolSAR Image Feature Extraction via Co-Regularized Graph Embedding
Dimensionality reduction (DR) methods based on graph embedding are widely used for feature extraction. For these methods, the weighted graph plays a vital role in the process of DR because it can characterize the data’s structure information.
Xiayuan Huang, Xiangli Nie, Hong Qiao
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We investigated nearest-neighbor density-based clustering for hyperspectral image analysis. Four existing techniques were considered that rely on a K-nearest neighbor (KNN) graph to estimate local density and to propagate labels through algorithm ...
Claude Cariou +2 more
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