Don't miss any moment of global AI innovation
Daily three-minute AI industry trends
AI industry milestones
AI monetization case sharing
AI image creation monetization cases
AI video creation monetization cases
AI audio creation monetization cases
AI content writing monetization cases
Free sharing of the latest AI tutorials
Shows total visits ranking of AI websites
Track fastest growing AI websites by traffic
Focus on AI websites with significant traffic drops
Shows weekly visits ranking of AI websites
AI websites most popular with US users
AI websites most popular with Chinese users
AI websites most popular with Indian users
AI websites most popular with Brazilian users
Total visits ranking of AI image generation websites
Total visits ranking of AI personal assistant websites
Total visits ranking of AI character generation websites
Total visits ranking of AI video generation websites
GitHub popular AI projects by total stars
GitHub popular AI projects by growth rate
GitHub popular AI developer ranking
GitHub popular AI organization ranking
GitHub popular deepseek open source projects
GitHub popular TTS open source projects
GitHub popular LLM open source projects
GitHub popular ChatGPT open source projects
Overview of GitHub popular AI open source projects
Knowledge-Augmented Language Models for Cause-Effect Relation Classification https://arxiv.org/abs/2112.08615
[NAACL 2021] QAGNN: Question Answering using Language Models and Knowledge Graphs ?
Knowledge-Aware Graph Networks for Commonsense Reasoning (EMNLP-IJCNLP 19)
[ICLR 2022 spotlight]GreaseLM: Graph REASoning Enhanced Language Models for Question Answering
Causal discovery algorithms and tools for implementing new ones
A Constrained Text Generation Challenge Towards Generative Commonsense Reasoning
A set of utilities for running few-shot prompting experiments on large-language models
Language Models of Code are Few-Shot Commonsense Learners (EMNLP 2022)
[Paper][ISWC 2021] Zero-shot Visual Question Answering using Knowledge Graph
The purpose of this repository is to introduce new dialogue-level commonsense inference datasets and tasks. We chose dialogues as the data source because dialogues are known to be complex and rich in commonsense.
Active Bayesian Causal Inference (Neurips'22)