attention-based prediction attention文献
latex插APJ文献报错
我在插入某种类型的参考文献(APJ系列)时，会收到报错： Undefined control sequence. \newblock \apjl GPT： 解决办法(临时的)： %自定义命令 \newcommand{\apjl}{{Astrophys. J. Lett.}} %对apj文献的引用 复 ......
latex参考文献不显示在pdf中
latex参考文献不显示在pdf中 下面是texstudio的“消息”一栏的输出： 开始 : bibtex "main".aux This is BibTeX, Version 0.99d (TeX Live 2022/dev/Debian) The top-level auxiliary file ......
【WALT】predict_and_update_buckets() 与 update_task_pred_demand() 代码详解
@目录【WALT】predict_and_update_buckets() 与 update_task_pred_demand() 代码详解代码展示代码逻辑⑴ 根据 runtime 给出桶的下标⑵ 根据桶的下标预测 pred_demand1. 如果任务刚被创建，直接结束2. 根据下标 bidx 和数 ......
tf.keras.layers.Attention: Dot-product attention layer, a.k.a. Luong-style attention.
tf.keras.layers.Attention( View source on GitHub ) Dot-product attention layer, a.k.a. Luong-style attention. Inherits From: Layer, Module tf.keras.la ......
Predict potential miRNA-disease associations based on bounded nuclear norm regularization
Predict potential miRNA-disease associations based on bounded nuclear norm regularization 2023/12/8 16:00:57 Predicting potential miRNA-disease associ ......
Self-attention小小实践
目录公式 1 不带权重的自注意力机制公式 2 带权重的自注意力机制 公式 1 不带权重的自注意力机制 \[Attention(X) = softmax(\frac{X\cdot{X^T}}{\sqrt{dim_X}})\cdot X \]示例程序： import numpy as np emb_di ......
LandBench 1.0: a benchmark dataset and evaluation metrics for data-driven land surface variables prediction
李老师对于landbench的，基准模型进行的论文。 里面对于变量，数据集的描述，写论文可以用。 题目： “LandBench 1.0: a benchmark dataset and evaluation metrics for data-driven land surface variables ......
Is Attention Better Than Matrix Decomposition?
Is Attention Better Than Matrix Decomposition? * Authors: [[Zhengyang Geng]], [[Meng-Hao Guo]], [[Hongxu Chen]], [[Xia Li]], [[Ke Wei]], [[Zhouchen Li ......
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation
SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation * Authors: [[Meng-Hao Guo]], [[Cheng-Ze Lu]], [[Qibin Hou]], [[Zhengning ......
CCNet: Criss-Cross Attention for Semantic Segmentation
CCNet: Criss-Cross Attention for Semantic Segmentation * Authors: [[Zilong Huang]], [[Xinggang Wang]], [[Yunchao Wei]], [[Lichao Huang]], [[Humphrey S ......
Dual Attention Network for Scene Segmentation：双线并行的注意力
Dual Attention Network for Scene Segmentation * Authors: [[Jun Fu]], [[Jing Liu]], [[Haijie Tian]], [[Yong Li]], [[Yongjun Bao]], [[Zhiwei Fang]], [[H ......
Attention Is All You Need
Attention Is All You Need * Authors: [[Ashish Vaswani]], [[Noam Shazeer]], [[Niki Parmar]], [[Jakob Uszkoreit]], [[Llion Jones]], [[Aidan N. Gomez]], ......
Expectation-Maximization Attention Networks for Semantic Segmentation 使用了EM算法的注意力
Expectation-Maximization Attention Networks for Semantic Segmentation * Authors: [[Xia Li]], [[Zhisheng Zhong]], [[Jianlong Wu]], [[Yibo Yang]], [[Zho ......
CBAM: Convolutional Block Attention Module
CBAM: Convolutional Block Attention Module * Authors: [[Sanghyun Woo]], [[Jongchan Park]], [[Joon-Young Lee]], [[In So Kweon]] doi:https://doi.org/10. ......
PSANet: Point-wise Spatial Attention Network for Scene Parsing双向注意力
PSANet: Point-wise Spatial Attention Network for Scene Parsing * Authors: [[Hengshuang Zhao]], [[Yi Zhang]], [[Shu Liu]], [[Jianping Shi]], [[Chen Cha ......
Object Tracking Network Based on Deformable Attention Mechanism
Object Tracking Network Based on Deformable Attention Mechanism Local library 初读印象 comment:: （DeTrack）采用基于可变形注意力机制的编码器模块和基于自注意力机制的编码器模块相结合的方式进行特征交互。基于 ......
BiFormer: Vision Transformer with Bi-Level Routing Attention 使用超标记的轻量ViT
alias: Zhu2023a tags: 超标记 注意力 rating: ⭐ share: false ptype: article BiFormer: Vision Transformer with Bi-Level Routing Attention * Authors: [[Lei Zhu] ......
A Deformable Attention Network for High-Resolution Remote Sensing Images Semantic Segmentation可变形注意力
A Deformable Attention Network for High-Resolution Remote Sensing Images Semantic Segmentation * Authors: [[Renxiang Zuo]], [[Guangyun Zhang]], [[Rong ......
Predicting Drug-Target Interactions. drug-target interactions prediction
2023 [j22] Junjun Zhang, Minzhu Xie:Graph regularized non-negative matrix factorization with L2,1 norm regularization terms for drug-target interactio ......
GCGP：Global Context and Geometric Priors for Effective Non-Local Self-Attention加入了上下文信息和几何先验的注意力
Global Context and Geometric Priors for Effective Non-Local Self-Attention * Authors: [[Woo S]] 初读印象 comment:: （GCGP）提出了一个新的关系推理模块，它包含了一个上下文化的对角矩阵和二维相 ......
Fully Attentional Network for Semantic Segmentation：FLANet
Fully Attentional Network for Semantic Segmentation * Authors: [[Qi Song]], [[Jie Li]], [[Chenghong Li]], [[Hao Guo]], [[Rui Huang]] 初读印象 comment:: (F ......
Flash-attention 2.3.2 支持 Windows了，但是我的2080ti是不支持的。
不久前Flash-attention 2.3.2 终于支持了 Windows，推荐直接使用大神编译好的whl安装 github.com/bdashore3/flash-attention/releasesstable diffusion webui flash-attention2性能测试 安装环境 ......
【论文解读】System 2 Attention提高大语言模型客观性和事实性
本文简要介绍了论文“System 2 Attention (is something you might need too) ”的相关工作。基于transformer的大语言模型（LLM）中的软注意很容易将上下文中的不相关信息合并到其潜在的表征中，这将对下一token的生成产生不利影响。为了帮助纠正... ......
The Devil Is in the Details: Window-based Attention for Image Compression
目录简介 简介 基于CNN的模型的一个主要缺点是 cNN结构不是为捕捉局部冗余而设计的，尤其是非重复纹理，这严重影响了重建质量。受视觉转换器（ViT）和Swin Transformer最新进展的启发，我们发现将局部感知注意机制与全局相关特征学习相结合可以满足图像压缩的期望。 介绍了一种更简单有效的基 ......
论文精读：STMGCN利用时空多图卷积网络进行移动边缘计算驱动船舶轨迹预测（STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network）
《STMGCN: Mobile Edge Computing-Empowered Vessel Trajectory Prediction Using Spatio-Temporal Multigraph Convolutional Network》 论文链接：https://doi.org/10. ......
引用近几年有较高影响力的文献
固氮、溶磷和解钾过程： [1] Bulgarelli, D., Schlaeppi, K., Spaepen, S., van Themaat, E. V. L., & Schulze-Lefert, P. (2013). Structure and functions of the bacteri ......
论文笔记: Attributed Graph Clustering: A Deep Attentional Embedding Approach
论文笔记: Attributed Graph Clustering: A Deep Attentional Embedding Approach 中文名称: 属性图聚类：一种深度注意力嵌入方法 论文链接: https://arxiv.org/abs/1906.06532 背景: 图聚类是发现网络 ......
Attention 2015-今
现在attention的热度已经过去了，基本上所有的attention都是transformer的kqv形式的，甚至只要说道attention，默认就是transformer的attention。 为避免遗忘历史，我这里做一个小总结。繁杂的att我就不去了解了，只了解下经典的。 以下以\(h_i\) ......
论文精读：基于具有时空感知的稀疏多图卷积混合网络的大数据驱动船舶轨迹预测（Big data driven trajectory prediction based on sparse multi-graph convolutional hybrid network withspatio-temporal awareness）
论文精读：基于具有时空感知的稀疏多图卷积混合网络的大数据驱动船舶轨迹预测 《Big data driven vessel trajectory prediction based on sparse multi-graph convolutional hybrid network with spati ......