https://github.com/rudongyu/logire
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
Papers with Code 아카이브
Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/runpeidong/act
ShapeLLM: Universal 3D Object Understanding for Embodied Interaction
https://github.com/runxinxu/childtuning
Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning
https://github.com/ruotianluo/zsl-gcn-pth
Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs
https://github.com/sail-sg/adan
Skywork-MoE: A Deep Dive into Training Techniques for Mixture-of-Experts Language Models
https://github.com/samleoqh/MultiModNet
https://github.com/sandai-org/magiattention
https://github.com/security-force-monitor/sfm-graph-extractor
https://github.com/seeyourmind/tkgelib
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
https://github.com/semantic-systems/NLIWOD
https://github.com/sevvalckc/Turkish-SAD
TurkishBERTweet: Fast and Reliable Large Language Model for Social Media Analysis
https://github.com/shamim-hussain/egt
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
https://github.com/shansongliu/M2UGen
Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning
https://github.com/shansongliu/MU-LLaMA
Music Understanding LLaMA: Advancing Text-to-Music Generation with Question Answering and Captioning
https://github.com/sheepover96/ProjE.torch
https://github.com/sheldonresearch/ProG
A Survey of Graph Meets Large Language Model: Progress and Future Directions
https://github.com/shihaozhaozsh/lavi-bridge
ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
https://github.com/shijx12/kqapro_baselines
https://github.com/showlab/WorldGUI
ASSISTGUI: Task-Oriented Desktop Graphical User Interface Automation
https://github.com/shreya1313/llm-unlearning
https://github.com/sib-swiss/sparql-examples-utils
https://github.com/sid-sundrani/differentiable-kb-qa
Expanding End-to-End Question Answering on Differentiable Knowledge Graphs with Intersection
https://github.com/sierra-research/tau-bench
$τ^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment
https://github.com/skyworkai/skywork
https://github.com/sl-93/SUPERVISED-CONTRASTIVE-LEARNING-FOR-PRE-TRAINED-LANGUAGE-MODEL-FINE-TUNING
Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning
https://github.com/smoothnlp/SmoothNLP
https://github.com/snap-stanford/KGReasoning
https://github.com/snap-stanford/neural-subgraph-learning-gnn
https://github.com/snap-stanford/ogb/tree/master/examples/linkproppred/citation2
Open Graph Benchmark: Datasets for Machine Learning on Graphs
https://github.com/snap-stanford/ogb/tree/master/examples/linkproppred/collab
Open Graph Benchmark: Datasets for Machine Learning on Graphs
https://github.com/snap-stanford/ogb/tree/master/examples/linkproppred/ddi
Open Graph Benchmark: Datasets for Machine Learning on Graphs
https://github.com/snap-stanford/ogb/tree/master/examples/linkproppred/ppa
Open Graph Benchmark: Datasets for Machine Learning on Graphs
https://github.com/snap-stanford/ogb/tree/master/examples/nodeproppred/arxiv
Open Graph Benchmark: Datasets for Machine Learning on Graphs
https://github.com/snap-stanford/ogb/tree/master/examples/nodeproppred/mag
Open Graph Benchmark: Datasets for Machine Learning on Graphs
https://github.com/snap-stanford/ogb/tree/master/examples/nodeproppred/papers100M
Open Graph Benchmark: Datasets for Machine Learning on Graphs
https://github.com/snap-stanford/ogb/tree/master/examples/nodeproppred/products
Open Graph Benchmark: Datasets for Machine Learning on Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.