Littérature scientifique sur le sujet « Auto-encodeur variationnel à graphes (VGAE) »
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Articles de revues sur le sujet "Auto-encodeur variationnel à graphes (VGAE)"
Patel, Neel, Nhat Le, Tan Nguyen, Fedaa Najdawi, Sandhya Srinivasan, Adam Stanford-Moore, Deeksha Kartik et al. « Abstract 4912 : Unsupervised detection of stromal phenotypes with distinct fibrogenic and inflamed properties in NSCLC ». Cancer Research 84, no 6_Supplement (22 mars 2024) : 4912. http://dx.doi.org/10.1158/1538-7445.am2024-4912.
Texte intégralZhu, Guixiang, Jie Cao, Lei Chen, Youquan Wang, Zhan Bu, Shuxin Yang, Jianqing Wu et Zhiping Wang. « A Multi-task Graph Neural Network with Variational Graph Auto-Encoders for Session-based Travel Packages Recommendation ». ACM Transactions on the Web, février 2023. http://dx.doi.org/10.1145/3577032.
Texte intégralDuy Nguyen, Viet Thanh, et Truong Son Hy. « Multimodal pretraining for unsupervised protein representation learning ». Biology Methods and Protocols, 18 juin 2024. http://dx.doi.org/10.1093/biomethods/bpae043.
Texte intégralYuan, Wei, Shiyu Zhao, Li Wang, Lijia Cai et Yong Zhang. « Online course evaluation model based on graph auto-encoder ». Intelligent Data Analysis, 21 mars 2024, 1–23. http://dx.doi.org/10.3233/ida-230557.
Texte intégralThèses sur le sujet "Auto-encodeur variationnel à graphes (VGAE)"
Belhadj, Djedjiga. « Multi-GAT semi-supervisé pour l’extraction d’informations et son adaptation au chiffrement homomorphe ». Electronic Thesis or Diss., Université de Lorraine, 2024. http://www.theses.fr/2024LORR0023.
Texte intégralThis thesis is being carried out as part of the BPI DeepTech project, in collaboration with the company Fair&Smart, primarily looking after the protection of personal data in accordance with the General Data Protection Regulation (RGPD). In this context, we have proposed a deep neural model for extracting information in semi-structured administrative documents (SSDs). Due to the lack of public training datasets, we have proposed an artificial generator of SSDs that can generate several classes of documents with a wide variation in content and layout. Documents are generated using random variables to manage content and layout, while respecting constraints aimed at ensuring their similarity to real documents. Metrics were introduced to evaluate the content and layout diversity of the generated SSDs. The results of the evaluation have shown that the generated datasets for three SSD types (payslips, receipts and invoices) present a high diversity level, thus avoiding overfitting when training the information extraction systems. Based on the specific format of SSDs, consisting specifically of word pairs (keywords-information) located in spatially close neighborhoods, the document is modeled as a graph where nodes represent words and edges, neighborhood connections. The graph is fed into a multi-layer graph attention network (Multi-GAT). The latter applies the multi-head attention mechanism to learn the importance of each word's neighbors in order to better classify it. A first version of this model was used in supervised mode and obtained an F1 score of 96% on two generated invoice and payslip datasets, and 89% on a real receipt dataset (SROIE). We then enriched the multi-GAT with multimodal embedding of word-level information (textual, visual and positional), and combined it with a variational graph auto-encoder (VGAE). This model operates in semi-supervised mode, being able to learn on both labeled and unlabeled data simultaneously. To further optimize the graph node classification, we have proposed a semi-VGAE whose encoder shares its first layers with the multi-GAT classifier. This is also reinforced by the proposal of a VGAE loss function managed by the classification loss. Using a small unlabeled dataset, we were able to improve the F1 score obtained on a generated invoice dataset by over 3%. Intended to operate in a protected environment, we have adapted the architecture of the model to suit its homomorphic encryption. We studied a method of dimensionality reduction of the Multi-GAT model. We then proposed a polynomial approximation approach for the non-linear functions in the model. To reduce the dimensionality of the model, we proposed a multimodal feature fusion method that requires few additional parameters and reduces the dimensions of the model while improving its performance. For the encryption adaptation, we studied low-degree polynomial approximations of nonlinear functions, using knowledge distillation and fine-tuning techniques to better adapt the model to the new approximations. We were able to minimize the approximation loss by around 3% on two invoice datasets as well as one payslip dataset and by 5% on SROIE