Mastering the game of Go with MCTS and Deep Neural Networks
Paper-reading notes: Mastering the game of Go with MCTS and Deep Neural Networks
Posts
CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping
Paper-reading notes: CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Paper-reading notes: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Learning‑based light field imaging
Paper-reading notes: Learning‑based light field imaging
A Survey of RAG
This post is primarily based on the survey “Retrieval-Augmented Generation for AI-Generated Content: A Survey” . I presents retrieval-augmented generation (RAG) in six parts: background, method, enhancement, applications, outlook, and takeaways. Background In recent years, we’ve seen a rapid surge in Artificial Intelligence Generated Content (AIGC), driven by large generative models that can produce text, code, images and even videos (Zhao et al. 2024 ). For text and code, widely used examples including GPT-style models and Anthropic’s Claude family (Achiam et al. 2023 , Anthropic 2024 ). For images, modern systems are often powered by diffusion-based text-image generation, including latent diffusion models (Ramesh et al. 2021 , Rombach et al. 2022 ). For video, OpenAI’s Sora is a prominent example of large-scale text-to-video generation (OpenAI 2024 ). ...
From Local to Global: A GraphRAG Approach to Query-Focused Summarization
Paper-reading notes: GraphRAG
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Paper-reading notes: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
A Bridging Model for Parallel Computation
Paper-reading notes: A Bridging Model for Parallel Computation
Attention is All You Need
Paper-reading notes: Attention is All You Need