https://github.com/IBM/RuDaS
공식 코드RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools
Papers with Code 아카이브
Inductive knowledge graph completion
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/IBM/helm-enterprise-benchmark
공식 코드https://github.com/IBM/prompt-declaration-language
공식 코드https://github.com/IBM/retrieve-write-slot-filling
공식 코드https://github.com/IBM/wikicausal
공식 코드WikiCausal: Corpus and Evaluation Framework for Causal Knowledge Graph Construction
https://github.com/IDEA-Emdoor-Lab/UniTTS
공식 코드UniTTS: An end-to-end TTS system without decoupling of acoustic and semantic information
https://github.com/IDEA-FinAI/Simple-HHEA
공식 코드https://github.com/INDElab/rgvae
공식 코드Knowledge Generation -- Variational Bayes on Knowledge Graphs
https://github.com/INK-USC/CALM
공식 코드Pre-training Text-to-Text Transformers for Concept-centric Common Sense
https://github.com/INK-USC/DArtNet
공식 코드Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution
https://github.com/INK-USC/HGN
공식 코드Learning Contextualized Knowledge Structures for Commonsense Reasoning
https://github.com/INK-USC/KagNet
공식 코드KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
https://github.com/INK-USC/MHGRN
공식 코드Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering
https://github.com/INK-USC/RE-Net
공식 코드Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
https://github.com/INK-USC/SalKG
공식 코드SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning
https://github.com/INK-USC/deceive-KG-models
공식 코드Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
https://github.com/ISU-PAAL/DeepDFA
공식 코드Dataflow Analysis-Inspired Deep Learning for Efficient Vulnerability Detection
https://github.com/IanSteenstra/llm-alcohol-counselor
공식 코드Virtual Agents for Alcohol Use Counseling: Exploring LLM-Powered Motivational Interviewing
https://github.com/IbrahimAlAzhar/FutureWorkGeneration
공식 코드FutureGen: LLM-RAG Approach to Generate the Future Work of Scientific Article
https://github.com/Ipazia-AI/latent-explorer
공식 코드Unveiling LLMs: The Evolution of Latent Representations in a Dynamic Knowledge Graph
https://github.com/JC230903/NLP-low-resource-pipeline-for-indian-languages/tree/main/pythonversion
공식 코드Low-Resource Language Processing: An OCR-Driven Summarization and Translation Pipeline
https://github.com/JD-AI-Research-Silicon-Valley/SACN
공식 코드End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
https://github.com/Jacob-Zhou/simple-csc
공식 코드A Training-free LLM-based Approach to General Chinese Character Error Correction
https://github.com/JasonForJoy/BRIEF
공식 코드BRIEF: Bridging Retrieval and Inference for Multi-hop Reasoning via Compression
https://github.com/JeanKaddour/WASAM
공식 코드https://github.com/Jeff20100601/GHNN_clean
공식 코드Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs
https://github.com/Jeffrey-Sardina/TWIG-I
공식 코드https://github.com/Jeffrey-Sardina/TWIG-TWM-dev
공식 코드https://github.com/JeongHun0716/MMS-LLaMA
공식 코드MMS-LLaMA: Efficient LLM-based Audio-Visual Speech Recognition with Minimal Multimodal Speech Tokens
https://github.com/JerichoWorld/JerichoWorld
공식 코드https://github.com/JesseTNRoberts/Large-Language-Model-Recall-Uncertainty-is-Modulated-by-the-Fan-Effect
공식 코드Large Language Model Recall Uncertainty is Modulated by the Fan Effect
https://github.com/JetBrains-Research/git-good-bench
공식 코드GitGoodBench: A Novel Benchmark For Evaluating Agentic Performance On Git
https://github.com/JiapengWu/TeMP
공식 코드TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion
https://github.com/JiapengWu/Time-Aware-Incremental-Embedding
공식 코드TIE: A Framework for Embedding-based Incremental Temporal Knowledge Graph Completion
https://github.com/JiaweiSheng/FAAN
공식 코드Adaptive Attentional Network for Few-Shot Knowledge Graph Completion
https://github.com/Jie0618/PhysicsRegression
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.