Conceptually, we conceive cells as living “information processing units” [44,45] that perform network-based “biological calculations” to regulate cell state [45,46,47]. KPNNs are designed to perform similar calculations in silico, predicting cell state from single-cell RNA-seq data in deep ...
The obtained triples will be embedded and input into the knowledge encoder with token embedding and position embedding. Then, the triple will be transformed into a vector matrix summed by ve ∈ RT ×K and vp ∈ RT ×K and input into the self- attention layer, where T represents the ...
However, existing works based on user-generated contents (UGCs) lack a comprehensive representation of textual features, particularly overlooking the latent semantic information embedded within textual content and neglecting the challenge of semantic distribution disparities across networks. Therefore, in this ...
[CVPR 2019] Knowledge-Embedded Routing Network for Scene Graph Generation. [IJCAI 2018] Representation Learning for Scene Graph Completion via Jointly Structural and Visual Embedding. [CVPR 2018] Neural Motifs: Scene Graph Parsing With Global Context.Retrieval...
5a. Besides, the prior knowledge from different sources is embedded into matrix Vk∈RN×dk, where dk is the number of dimensions of the target vector space of the knowledge embedding, shown as the purple matrices in Fig. 5a. By concatenating the column vectors from the two matrices that ...
However, it is a challenge to deploy these cumbersome deep models on devices with limited resources, e.g., mobile phones and embedded devices, not only because of the high computational complexity but also the large storage requirements. To this end, a variety of model compression and ...
We leverage the anatomy knowledge embedded in CT, which features a 3D volume with clearly visible anatomies. Our key idea is to embed CT priori decomposition knowledge into the latent space of unpaired CXR autoencoder. Specifically, we train DecGAN with a decomposition loss, adversarial losses, ...
A relation hashing network embedded with prior features for skin lesion classification. In: Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10. Springer, Heidelberg, Germany.; 2019. ...
Digital twins are virtual replicas of real physical entities in computers. They can be considered as abstract digital models of data and behavior for objects of interest. Nevertheless, they are not perfectly consistent with conventional data or simulation models because they achieve prediction and optim...
A relation hashing network embedded with prior features for skin lesion classification. In: Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings 10. Springer, Heidelberg, Germany.; 2019. ...