1. Information Title: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Link: ViT Paper Source: International Conference on Learning Representations (ICLR) Date: 2020.10.22 2.

1. Information Title: Locating and Editing Factual Associations in GPT Link: ROME Paper Source: Conference on Neural Information Processing Systems (NeurIPS) Date: 2022.02.10 2. Summary 本文提出了一种方法，用于在训

1. Information Title: Improving Language Understanding by Generative Pre-Training Link: GPT V1 Paper Source: OpenAI Technical Report Date: 2018 2. Summary 本文提出了一种基于 Transformer 解码器的生成式语言模型，该模型通过在大规模无

1. Information Title: BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding Link: BERT Paper Source: The North American Chapter of the Association for Computational Linguist

1. Information Title: Attention Is All You Need Link: Transformer Source: Conference on Neural Information Processing Systems (NeurIPS) Date: 2017.06.12 2. Summary 本文提出了 Transformer 模型，这是一种用于序列转换任务（特别

1. Information Title: ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design Link: ShuffleNet V2 Paper Source: European Conference on Computer Vision (ECCV) Date: 2018.07.30 2. Summ

1. Information Title: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Link: MobileNets: Efficient CNNs for Mobile Applications Source: IEEE Conference on Computer Vi

1. Information Title: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Link: ShuffleNet Paper Source: IEEE Conference on Computer Vision and Pattern Recognition (CVPR

1. Information Title: Densely Connected Convolutional Networks Link: DenseNet Paper Source: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Date: 2016.08.25 2. Summary 本文提出了 DenseNet

1. Information Title: Aggregated Residual Transformations for Deep Neural Networks Link: ResNeXt Paper Source: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Date: 2016.11.16 2. Sum