https://github.com/MS-Mind/MS-Code-01/tree/main/rotate
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
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/MS-P3/code3/tree/main/jamba
https://github.com/MS-P3/code5/tree/main/llama3
An empirical study of LLaMA3 quantization: from LLMs to MLLMs
https://github.com/MS-P3/code6/tree/main/rag
RAG and RAU: A Survey on Retrieval-Augmented Language Model in Natural Language Processing
https://github.com/ManasRMohanty/DS5500-capstone
https://github.com/MaxenceGiraud/TensorTemporalKG
Tucker decomposition-based Temporal Knowledge Graph Completion
https://github.com/MichSchli/RelationPrediction
https://github.com/Microsoft/gated-graph-neural-network-samples
https://github.com/Mind23-2/MindCode-134
CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis
https://github.com/Mind23-2/MindCode-136
ERNIE: Enhanced Language Representation with Informative Entities
https://github.com/MindCode-4/code-12/tree/main/isr
ISR-LLM: Iterative Self-Refined Large Language Model for Long-Horizon Sequential Task Planning
https://github.com/MindCode-4/code-14/tree/main/CAJ
Learn from Downstream and Be Yourself in Multimodal Large Language Model Fine-Tuning
https://github.com/MindCode-4/code-4/tree/main/qwen2_vl
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
https://github.com/MindSpore-paper-code-2/code400/tree/main/DeepText
https://github.com/MindSpore-paper-code-3/code10/tree/main/guided_anchoring
https://github.com/MindSpore-paper-code-3/code9/tree/main/DeepText
https://github.com/MindSpore-paper-code-3/code9/tree/main/dgu
Constructing Temporal Dynamic Knowledge Graphs from Interactive Text-based Games
https://github.com/MindSpore-paper-code-3/code9/tree/main/rotate
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
https://github.com/MindSpore-scientific-2/code-10/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-10/tree/main/QuantExplainNLP_AlgoAnalysis
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
https://github.com/MindSpore-scientific-2/code-10/tree/main/RGNN
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
https://github.com/MindSpore-scientific-2/code-10/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
https://github.com/MindSpore-scientific-2/code-11/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
https://github.com/MindSpore-scientific-2/code-14/tree/main/jamba
https://github.com/MindSpore-scientific-2/code-2/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
https://github.com/MindSpore-scientific-2/code-4/tree/main/QuantExplainNLP_AlgoAnalysis
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-5/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-8/tree/main/MicAugment
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-8/tree/main/QuantExplainNLP_AlgoAnalysis
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/MindSpore-scientific-2/code-8/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
https://github.com/MindSpore-scientific-2/code-8/tree/main/text-to-music
https://github.com/MindSpore-scientific/code-10/tree/main/Last-Query-Transformer-RNN
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
https://github.com/MindSpore-scientific/code-13/tree/main/QuantExplainNLP_AlgoAnalysis
NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark
https://github.com/MindSpore-scientific/code-13/tree/main/UE-Unified-Embedding-Battle-Tested-Feature
https://github.com/MindSpore-scientific/code-8/tree/main/Last-Query-Transformer-RNN
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
https://github.com/MindSpore-scientific/code-8/tree/main/shallow-rnns
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.