LPRE需要额外的一层MLP mapper来将Laplacian eigenvectors映射到LMs的text space; Graph Neural Netwoks (GNN):首先,通过一个frozen encoders来计算邻居embeddings,然后对这些embeddings依据graph structure运行GNN【也就是邻居embedding是特征句子,graph struture是邻接矩阵】。之后,我们将输出的GNN embeddings作为position enc...
Graph-BERT model 3 THE PROPOSED PRE-TRAINING MODEL: PMGT PMGT架构 四个主要的组件: contextual neighbors sampling(上下文临近采样) node embedding initialization(节点嵌入初始化) transformer-based encoder(基于变压器的编码器) graph reconstruction(图重建) MCN采样算法 3.1 Contextual Neighbors Sampling 算法上的考...
transformer对于多维度情况（因为要时空一起处理的话，维度就是ST）扩大容量很困难，主要原因是self attent...
The proposed contrastive graph Transformer representation model incorporates the heterogeneity map constrained by T1-to-T2-weighted (T1w/T2w) to improve the model fit to structure-function interactions. The experimental results with multimodal resting state brain measurements demonstrate the proposed method...
We introduce GPDRP, a novel multimodal framework for DRP, which leverages Graph Convolutional Networks in conjunction with Graph Transformer and deep neural networks. The performance of GPDRP is demonstrated using the CCLE/GDSC dataset, and it outperforms two recently published models, Precily and ...
The recent advancement of pre-trained Transformer models has propelled the development of effective text mining models across various biomedical tasks. However, these models are primarily learned on the textual data and often lack the domain knowledge of the entities to capture the context beyond the...
以便我们能够准确地建模方程式2中所需的条件分布的因子分解。第二个组件是一个基于自回归Transformer的...
FLAVA 是一个基础多模态模型,由基于 transformer 的图像和文本编码器以及基于 transformer 的多模态融合模块组成。 FLAVA 在单模态和多模态数据上都进行了预训练,且这些数据的损失 (loss) 各不相同,包括掩码的语言、图像和多模态模型 loss,要求模型从其上下文中重建原始输入(自监督学习)。
FLAVA 是一个基础多模态模型,由基于 transformer 的图像和文本编码器以及基于 transformer 的多模态融合模块组成。 FLAVA 在单模态和多模态数据上都进行了预训练,且这些数据的损失 (loss) 各不相同,包括掩码的语言、图像和多模态模型 loss,要求模型从其上下文中重建原始输入(自监督学习)。
🔥🔥🔥VITA: Towards Open-Source Interactive Omni Multimodal LLM [📽 VITA-1.5 Demo Show! Here We Go! 🔥] [📖 VITA-1.5 Paper (Comming Soon)] [🌟 GitHub] [🤗 Hugging Face] [🍎 VITA-1.0] [💬 WeChat (微信)] We are excited to introduce theVITA-1.5, a more powerful and...