https://github.com/snap-stanford/ogb/tree/master/examples/nodeproppred/proteins
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Papers with Code 아카이브
Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 평가표
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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/songzeceng/KGNNLS_DEMO
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
https://github.com/sparsh-ai/RecommenderSystems
Ekar: An Explainable Method for Knowledge Aware Recommendation
https://github.com/spcl/knowledge-graph-of-thoughts
https://github.com/squeezeailab/kvquant
IntactKV: Improving Large Language Model Quantization by Keeping Pivot Tokens Intact
https://github.com/stanford-oval/storm
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models
https://github.com/stanfordnlp/cocoa
Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
https://github.com/stzhang-patrick/arcmmlu
ArcGPT: A Large Language Model Tailored for Real-world Archival Applications
https://github.com/suklav/Artificial-Intelligence/blob/main/Nqueens_DFS.cpp
https://github.com/sunhwan-lee/recommender_system
Recommendation Through Mixtures of Heterogeneous Item Relationships
https://github.com/sunnweiwei/co-bench
A Comprehensive Evaluation of Contemporary ML-Based Solvers for Combinatorial Optimization
https://github.com/sunsmarterjie/chatterbox
GPT4RoI: Instruction Tuning Large Language Model on Region-of-Interest
https://github.com/susurrant/flow-imputation
https://github.com/susurrant/spatial-interaction-modeling
https://github.com/svakulenk0/semantic_coherence
https://github.com/svjan5/gnns-for-nlp
Neural Graph Embedding Methods for Natural Language Processing
https://github.com/taesunwhang/MVAN-VisDial
https://github.com/tanny411/llm-reliability-and-consistency-evaluation
TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliability
https://github.com/temibabs/I-RGCN
Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing
https://github.com/tencentqqgylab/ella
ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignment
https://github.com/tensorflow/neural-structured-learning/tree/master/research/kg_hyp_emb
https://github.com/terwilligers/knowledge-graph-recommender
Explainable Reasoning over Knowledge Graphs for Recommendation
https://github.com/teticio/llama-squad
https://github.com/theblackcat102/language-models-are-knowledge-graphs-pytorch
https://github.com/theoremone/llm-benchmarker-suite
https://github.com/thiviyanT/torch-rgcn
https://github.com/thorMax/AIPaperWriter
PaperRobot: Incremental Draft Generation of Scientific Ideas
https://github.com/thu-esis/jiuzhou
TagRouter: Learning Route to LLMs through Tags for Open-Domain Text Generation Tasks
https://github.com/thu-keg/eakit
https://github.com/thu-keg/korc
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/thudm/alignbench
https://github.com/thudm/chatglm-6b
https://github.com/tianyic/only_train_once
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge Recovery
https://github.com/timonziegenbein/inappropriateness-mitigation
https://github.com/tkipf/gae
https://github.com/togethercomputer/moa
Mixture-of-Agents Enhances Large Language Model Capabilities
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.