1. 开山模型

• Playing Atari with Deep Reinforcement Learning

• FCN:Fully Convolutional Networks for Semantic Segmentation

• U-Net:Convolutional Networks for Biomedical Image Segmentation

• GAN:Generative Adversarial Nets

• Attention Is All You Need

• GPT-1:Improving Language Understanding by Generative Pre-Training

• InstructGPT:Training language models to follow instructions with human feedback

• BERT:Pre-training of Deep Bidirectional Transformers for Language Understanding

• BART:Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

• T5:Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

• ELMo:Deep contextualized word representations

• ViT:AN IMAGE IS WORTH 16X16 WORDS:TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE

• Distilling the Knowledge in a Neural Network

• DeiT:Training data-efficient image transformers & distillation through attention

• Swin Transformer:Hierarchical Vision Transformer using Shifted Windows

• DETR:End-to-End Object Detection with Transformers

• CLIP:Learning Transferable Visual Models From Natural Language Supervision

• VAE:Auto-Encoding Variational Bayes

• VQ-VAE:Neural Discrete Representation Learning

• VQ-VAE2:Generating Diverse High-Fidelity Images with VQ-VAE-2

• KAN:Kolmogorov–Arnold Networks

• Pixel RNN:Pixel Recurrent Neural Networks

• Conditional Image Generation with PixelCNN Decoders

• GQA:Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints

• FlashAttention:Fast and Memory-Efficient Exact Attention with IO-Awareness

• Efficient Memory Management for Large Language Model Serving with PagedAttention