Papers with Code 아카이브
Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 5
- 평가표
- 4
- 보존된 평가 기록
- 19
A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/zihanzhaosjtu/librisqa
PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
https://github.com/ziyuguo99/image-generation-cot
MAVIS: Mathematical Visual Instruction Tuning with an Automatic Data Engine
https://github.com/zjs123/streame
Diachronic Embedding for Temporal Knowledge Graph Completion
https://github.com/zju-dbl/metakg
https://github.com/zjukg/umaea
Rethinking Uncertainly Missing and Ambiguous Visual Modality in Multi-Modal Entity Alignment
https://github.com/zjunlp/promptkgc
LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings
https://github.com/zrrskywalker/point-bind
https://github.com/ztcoalson/if-guide
Studying Large Language Model Generalization with Influence Functions
https://github.com/zyang1580/binllm
MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models
https://github.com/zyjcs/KGE-HAE
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://huggingface.co/datasets/alykassem/DSC-Benchmark
How Robust Are Router-LLMs? Analysis of the Fragility of LLM Routing Capabilities
https://huggingface.co/datasets/jjjjh/MM-Instruct
MM-Instruct: Generated Visual Instructions for Large Multimodal Model Alignment
https://huggingface.co/datasets/liuhuijie6410/OmniDish-Edit-35K
Omni-Dish: Photorealistic and Faithful Image Generation and Editing for Arbitrary Chinese Dishes
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.