【文献阅读】P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

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2023

04-23

【文献阅读】P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

04-18

【文献阅读】P-tuning: GPT Understands, Too

04-03

【文献阅读】GLM: General Language Model Pretraining with Autoregressive Blank Infilling

03-30

【文献阅读】ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

03-30

【文献阅读】DALL·E 2: Hierarchical Text-Conditional Image Generation with CLIP Latents

03-15

【文献阅读】KOSMOS-1: Language Is Not All You Need: Aligning Perception with Language Models

03-08

【文献阅读】UniLM: Unified Language Model Pre-training for Natural Language Understanding and Generation

03-06

【文献阅读】Transformer: Attention Is All You Need

02-27

【文献阅读】BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

02-27

【文献阅读】SimCLR: A Simple Framework for Contrastive Learning of Visual Representations