Task Generation Scheme for the Meta-Unsupervised Algorithm

Zero and Few shot named entity & relationships recognition

Study and review papers of journals and conferences.

总结Prompt&LLM论文，开源数据&模型，AIGC应用

Geodesic-Former: a Geodesic-Guided Few-shot 3D Point Cloud Instance Segmenter (ECCV 2022)

Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments (ECCV 2022)

Few-shot Object Counting and Detection (ECCV 2022)

AI-powered tool for effortless, high-quality results from simple prompts. No prompt engineering skills required.

Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.

Papers and code related to 'Less Than One'-Shot (LO-Shot) Learning

Automatically fill in missing values in tabular data using in-context learning techniques.

Efficient few-shot learning with Sentence Transformers

Service to import data from various sources and index it in AI Search. Increases data relevance and reduces final size by 90%+. Useful for RAG scenarios with LLM. Hosted in Azure with serverless architecture.

Uses Groq's API to explore various prompt engineering techniques for sentiment analysis, extracting structured outputs, etc

[Me-FoMo ICLR 2023 - Oral] Exploring Demonstration Ensembling for In-context Learning

Code Repository for "SFT: Few-Shot Learning via Self-Supervised Feature Fusion With Transformer"

Kistmat-AI is an advanced machine learning model designed to solve a wide range of mathematical problems, from elementary arithmetic to university-level calculus. This project demonstrates the application of curriculum learning in AI, allowing the model to progressively tackle more complex mathematical concepts.

Meta Learning implementations via PyTorch (without any other frameworks)

A collection of AWESOME things about domian adaptation