Results 41 to 50 of about 533,476 (267)

Graph Propagation Transformer for Graph Representation Learning

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
This paper presents a novel transformer architecture for graph representation learning. The core insight of our method is to fully consider the information propagation among nodes and edges in a graph when building the attention module in the transformer blocks. Specifically, we propose a new attention mechanism called Graph Propagation Attention (GPA).
Chen, Zhe, Tan, Hao, Wang, Tao, Shen, Tianrun, Lu, Tong, Peng, Qiuying, Cheng, Cheng, Qi, Yue   +7 more
openaire   +2 more sources

GFF-CARVING: Graph Feature Fusion for the Recognition of Highly Varying and Complex Balinese Carving Motifs

IEEE Access, 2022
The recognition of Balinese carving motifs is challenging due to the highly varying and interrelated motifs of Balinese carvings and in addition to the scantiness of Balinese carving data.
I Wayan Agus Surya Darma, Nanik Suciati, Daniel Siahaan   +2 more
doaj   +1 more source

GraphGAN: Graph Representation Learning with Generative Adversarial Nets

, 2017
The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying ...
Guo, Minyi, Wang, Hongwei, Wang, Jia, Wang, Jialin, Xie, Xing, Zhang, Fuzheng, Zhang, Weinan, Zhao, Miao   +7 more
core   +1 more source

Zero Shot Learning with the Isoperimetric Loss

, 2019
We introduce the isoperimetric loss as a regularization criterion for learning the map from a visual representation to a semantic embedding, to be used to transfer knowledge to unknown classes in a zero-shot learning setting.
Bertozzi, Andrea, Deutsch, Shay, Soatto, Stefano   +2 more
core   +1 more source

Latent Semantic Learning with Structured Sparse Representation for Human Action Recognition

, 2011
This paper proposes a novel latent semantic learning method for extracting high-level features (i.e. latent semantics) from a large vocabulary of abundant mid-level features (i.e.
Balasubramanian, Belkin, Blei, Cheng, Donoho, Hofmann, Jenatton, Lafon, Liu, Lu, Niebles, Olshausen, Parameswaran, Tibshirani, Turaga, Wang, Wright, Yan, Yuxin Peng, Zhiwu Lu   +19 more
core   +1 more source

Dual Graph Representation Learning

, 2020
Graph representation learning embeds nodes in large graphs as low-dimensional vectors and is of great benefit to many downstream applications. Most embedding frameworks, however, are inherently transductive and unable to generalize to unseen nodes or learn representations across different graphs.
Zhu, Huiling, Luo, Xin, Zhuo, Hankz Hankui   +2 more
openaire   +2 more sources

Graph-based Molecular Representation Learning

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which the downstream tasks (e.g., property prediction) can be performed. Recently, MRL has achieved considerable
Guo, Zhichun, Guo, Kehan, Nan, Bozhao, Tian, Yijun, Iyer, Roshni G., Ma, Yihong, Wiest, Olaf, Zhang, Xiangliang, Wang, Wei, Zhang, Chuxu, Chawla, Nitesh V.   +10 more
openaire   +2 more sources

Dual targeting of RET and SRC synergizes in RET fusion‐positive cancer cells

Molecular Oncology, EarlyView.
Despite the strong activity of selective RET tyrosine kinase inhibitors (TKIs), resistance of RET fusion‐positive (RET+) lung cancer and thyroid cancer frequently occurs and is mainly driven by RET‐independent bypass mechanisms. Son et al. show that SRC TKIs significantly inhibit PAK and AKT survival signaling and enhance the efficacy of RET TKIs in ...
Juhyeon Son, Lily L. Remsing Rix, Bin Fang, Eric A. Welsh, Nicole V. Bremer, Valentina Foglizzo, Paola Roa, Nickole Sigcha‐Coello, Yi Liao, Eric B. Haura, Alexander Drilon, John M. Koomen, Emiliano Cocco, Uwe Rix   +13 more
wiley   +1 more source

WMGCN: Weighted Meta-Graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks

IEEE Access, 2020
Network embedding has been an effective tool to analyze heterogeneous networks (HNs) by representing nodes in a low-dimensional space. Although many recent methods have been proposed for representation learning of HNs, there is still much room for ...
Jinli Zhang, Zongli Jiang, Zheng Chen, Xiaohua Hu   +3 more
doaj   +1 more source

Methylation biomarkers can distinguish pleural mesothelioma from healthy pleura and other pleural pathologies

Molecular Oncology, EarlyView.
We developed and validated a DNA methylation–based biomarker panel to distinguish pleural mesothelioma from other pleural conditions. Using the IMPRESS technology, we translated this panel into a clinically applicable assay. The resulting two classifier models demonstrated excellent performance, achieving high AUC values and strong diagnostic accuracy.
Janah Vandenhoeck, Nele De Meulenaere, Thomas Vanpoucke, Joe Ibrahim, Dieter Peeters, Suresh Krishan Yogeswaran, Wen Wen, Paul Van Schil, Jeroen M. H. Hendriks, Jo Raskin, Jan van Meerbeeck, Guy Van Camp, Ken Op de Beeck   +12 more
wiley   +1 more source