daily reading 20
- 【DAILY READING】A Survey on Knowledge Graphs：Representation Acquisition and Applications
- 【DAILY READING】LLaMA：Open and Efficient Foundation Language Models
- 【DAILY READING】Deep Residual Learning for Image Recognition
- 【DAILY READING】Deep contextualized word realizations
- 【DAILY READING】Language Is Not All You Need：Aligning Perception with Language Models
- 【DAILY READING】In-Context Pretraining：Language Modeling Beyond Document Boundaries
- 【DAILY READING】CodeChain：Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules
- 【DAILY READING】GPT-Fathom：Benchmarking Large Language Models to Decipher the Evolutionary Path towards GPT-4 And Beyond
- 【DAILY READING】CodeFusion：A Pre-trained Diffusion Model for Code Generation
- 【DAILY READING】Sequence to Sequence Learning with Neural Networks
- 【DAILY READING】大模型周报丨Table-GPT、3D-GPT、AgentTuning、MusicAgent等新工作重磅来袭
- 【DAILY READING】Neural networks and physical systems with emergent collective computational abilities
- 【DAILY READING】Deep Reinforcement Learning from Human Preferences
- 【DAILY READING】An Initial Exploration of Theoretical Support for Language Model Data Engineering. Part 1：Pretraining
- 【DAILY READING】Augmented Language Models：a Survey
- 【DAILY READING】Textbooks Are All You Need
- 【DAILY READING】Efficient Estimation of Word Representations in Vector Space
- 【DAILY READING】iTransformer：Inverted Transformer Are Effective for Time Series Forecasting
- 【DAILY READING】DistilBERT, a distilled version of BERT：smaller, faster, cheaper and lighter
- 【DAILY READING】Distilling the Knowledge in a Neural Network