这是第一个通过聚焦细胞及其相互作用来处理组织病理学图像的多模态图神经网络。 2. 通过定义共享上下文处理的新概念,我们设计了一个多模态图transformer (AMIGO),它利用分层结构为一个患者生成单一的表示,实现了细胞水平和组织水平信息之间的动态聚焦。 3. 稀疏处理降低了计算复杂度和计算成本,同时使模型具有较强的鲁...
In this regard, we propose an integrated framework guiding diffusion process at each node by a downstream transformer where both short- and long-range properties of graphs are aggregated via diffusion-kernel and multi-head attention respectively. We demonstrate the superiority of our model by ...
为了解决之前基于BEV的方法中的特征未对齐问题,提出了一个鲁棒的融合框架,名为GraphBEV,如图2所示。从不同的传感器(包括LiDAR和相机)获取输入,我们首先应用特定于模态的编码器,Swin-Transformer作为相机编码器,Second作为LiDAR编码器,以提取他们各自的特点。然后,通过我们提出的LocalAlign模块将相机特征转换为相机BEV特征,...
Graph Transformer for Graph-to-Sequence Learning. AAAI 2020. paper Deng Cai, Wai Lam. Multi-‐label Patent Categorization with Non-‐local Attention-‐based Graph Convolutional Network. AAAI 2020. paper Pingjie Tang, Meng Jiang, Bryan (Ning) Xia, Jed Pitera, Jeff Welser, Nitesh Ch...
Practically, those are geometric GNNs pre-trained in the multi-task mode to predict the energy (or forces) of a certain atomic structure. Another notable mention goes to Equiformer V2 (Liao et al) as a strong equivariant transformer that holds SOTA in many tasks including ...
LanczosNet: Multi-Scale Deep Graph Convolutional Networks.ICLR 2019.paper Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel. Invariant and Equivariant Graph Networks.ICLR 2019.paper Haggai Maron, Heli Ben-Hamu, Nadav Shamir, Yaron Lipman. ...
Type-adaptive graph Transformer for heterogeneous information networks Article 24 August 2024 A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention Chapter © 2021 MG2Vec+: A multi-headed graph attention network for multigraph embedding Article 26 September 2022 1...
论文使用Open pre-trained transformer(OPT-125m)为基本LM,读取input section text并生成摘要。 对于获取邻域信息的文本和图像编码器,使用来自CLIP 的文本/图像编码器。 论文微调了OPT而文本/图像编码器在所有实验中都被冻结。 论文在验证集上测量了BLEU-4 、ROUGE-L和CIDEr分数。
AttentionMGT-DTA A multi-modal drug-target affinity prediction using graph transformer and attention mechanism.pdf 2.8M · 百度网盘 摘要 药物靶向亲和力(DTA)的准确预测是药物发现和设计的关键步骤。传统的实验非常昂贵和耗时。近年来,深度学习方法在DTA预测中取得了显著的性能改进。然而,基于深度学习的模型面临的...
In this paper, a transformer-based model, named Mass Spectrum Transformer (MST), is proposed to perform quantitative analysis of molecular spectra, then it is combined with the graph neural network to form a multi-modal data fusion model TransG-Net for accurate molecular properties prediction. ...