In this paper, a novel model combining transfer learning and dynamic feedback called deep embedded clustering with transformer(DEC-transformer) is proposed. To better capture the semantic relationships between
Efficient Deep Embedded Subspace Clustering Jinyu Cai1,3, Jicong Fan2,3∗, Wenzhong Guo1, Shiping Wang1, Yunhe Zhang1, Zhao Zhang4 1College of Computer and Data Science, Fuzhou University, China 2School of Data Science, The Chinese University of Hong Kong (Shenzhen), China 3Shenzhen ...
A more detailed evaluation of the impacts of these embedded inconsistencies has been presented in [67]. Similar inconsistencies that were overlooked during the model calibration also affected the results of our experiments. For example, in the EE test, we noticed that the DRL controller was ...
Improved deep convolutional embedded clustering with re-selectable sample training 2022, Pattern Recognition Citation Excerpt : For example, we can apply the model to medical image processing and other applications [38,39]. Show abstract Functional magnetic resonance imaging, deep learning, and Alzheimer...
RDEC: Integrating Regularization into Deep Embedded Clustering for Imbalanced DatasetsRDECACML 2018- Deep Embedded Clustering with Data AugmentationDEC-DAACML 2018TensorFlow Deep adversarial subspace clusteringDASCCVPR 2018- Deep Clustering for Unsupervised Learning of Visual FeaturesDeepClusterECCV 2018Pytorch ...
A Unified Library for Deep Graph Clustering machine-learningdata-miningdeep-learninggraph-clusteringgraph-attention-networksself-supervised-learninggraph-convolutional-neural-networksdeep-clusteringdeep-graph-clusteringdeep-graph-clustering-framework UpdatedMay 22, 2025 ...
Data mining and analysis are critical for preventing or mitigating natural hazards. However, data availability in natural hazard analysis is experiencing u
For the structural models, the input is a crystal definition, which encodes the lattice, structure and atom definitions. Each atom is represented as a single node in the graph. Edges are defined when the interatomic distance is less than a user-defined threshold. Nodes are embedded by atom ty...
Toward calculation of amino acid-wise output, the atom-wise representation is pooled at the amino acid scale and concatenated with embedded amino acid-level information (either amino acid type or position–weight matrix). The constituting atoms of an amino acid have various types and may play di...
[40]. In previous chemical applications like retrosynthesis and fragment assembly, chemical structures were often converted into SMILES strings that ignored spatial information naturally embedded in chemical 3D conformers. Also, none of them considered the protein target information during the transformation...