#大语言模型#《李宏毅深度学习教程》(李宏毅老师推荐👍,苹果书🍎),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases
#计算机科学#The GitHub repository for the paper "Informer" accepted by AAAI 2021.
翻译 - AAAI 2021(最佳论文奖)接受了论文“ Informer”的GitHub存储库。
#Awesome#An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)
#计算机科学#My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy...
翻译 - 我对原始GAT论文的执行(Veličković等)。Jupyter Notebook即将面世,也是一个归纳示例。另外,我还包括了用来查看Cora数据集,GAT嵌入和注意力机制的Playground.py文件。
#自然语言处理#Datasets, tools, and benchmarks for representation learning of code.
翻译 - 用于代码表示学习的数据集,工具和基准。
The implementation of DeBERTa
翻译 - DeBERTa的实施
CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).
翻译 - CCNet:语义分割的跨界关注(ICCV 2019)。
#计算机科学#Recent Transformer-based CV and related works.
#计算机科学#Implementation of various self-attention mechanisms focused on computer vision. Ongoing repository.
#计算机科学#[CVPR 2025] Official PyTorch Implementation of MambaVision: A Hybrid Mamba-Transformer Vision Backbone
list of efficient attention modules
翻译 - 有效关注模块列表
#自然语言处理#Pre-training of Deep Bidirectional Transformers for Language Understanding: pre-train TextCNN
#计算机科学#[ICLR 2024] Official PyTorch implementation of FasterViT: Fast Vision Transformers with Hierarchical Attention
Text classification using deep learning models in Pytorch
(ICLR 2022 Spotlight) Official PyTorch implementation of "How Do Vision Transformers Work?"
A PyTorch implementation of Speech Transformer, an End-to-End ASR with Transformer network on Mandarin Chinese.
Universal Graph Transformer Self-Attention Networks (TheWebConf WWW 2022) (Pytorch and Tensorflow)
A Pytorch Implementation of "Attention is All You Need" and "Weighted Transformer Network for Machine Translation"