learndeep
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  • Introduction
  • 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
  • 2. 自然语言处理
  • 3. 计算机视觉
    • DDPM:Denoising Diffusion Probabilistic Models
    • DDIM:DENOISING DIFFUSION IMPLICIT MODELS
    • CDM:Diffusion Models Beat GANs on Image Synthesis
    • SCORE-BASED GENERATIVE MODELING THROUGH STOCHASTIC DIFFERENTIAL EQUATIONS
    • CDM:CLASSIFIER-FREE DIFFUSION GUIDANCE
    • RePaint:Inpainting using Denoising Diffusion Probabilistic Models
    • Stable Diffusion:High-Resolution Image Synthesis with Latent Diffusion Models
    • Semi-Parametric Neural Image Synthesis
    • Stable Video Diffusion:Scaling Latent Video Diffusion Models to Large Datasets
    • DiT:Scalable Diffusion Models with Transformers
    • Sora:A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
    • Segment Anything
    • ControlNet:Adding Conditional Control to Text-to-Image Diffusion Models
    • T2I-Adapter:Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
    • IP-Adapter:Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models
    • Imagen:Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding
    • DALL·E:Zero-Shot Text-to-Image Generation
    • DALL·E2:unCLIP:Hierarchical Text-Conditional Image Generation with CLIP Latents
    • DALL·E3:Improving Image Captioning with Better Use of Captions
    • VQ-GAN:Taming Transformers for High-Resolution Image Synthesis
  • 4. 强化学习
  • 5. 大模型微调
    • Adapter tuning:Parameter-Effificient Transfer Learning for NLP
    • Prefix-Tuning:Optimizing Continuous Prompts for Generation
    • Prompt Tuning:The Power of Scale for Parameter-Effificient Prompt Tuning
    • P-Tuning:GPT Understands, Too
    • P-Tuning v2:Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
    • LoRA:Low-rank adaptation of large language models
    • IA3:Few-Shot Parameter-Effificient Fine-Tuning is Better and Cheaper than In-Context Learning
  • 6. 参考文献
  • 7. 贡献
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4. 强化学习

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Last updated 4 months ago