https://github.com/mims-harvard/Madrigal
공식 코드Multimodal AI predicts clinical outcomes of drug combinations from preclinical data
Papers with Code 아카이브
Knowledge Graph Completion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 7
- 보존된 평가 기록
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Knowledge graphs $G$ are represented as a collection of triples $\\{(h, r, t)\\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mims-harvard/ToolUniverse
공식 코드TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools
https://github.com/mims-harvard/TxAgent
공식 코드TxAgent: An AI Agent for Therapeutic Reasoning Across a Universe of Tools
https://github.com/minaek/reward_design_with_llms
공식 코드https://github.com/mindverse/rag-tuned-llm
공식 코드https://github.com/ming-k9/mft
공식 코드https://github.com/mingyue-cheng/bingjian
공식 코드Towards Personalized Evaluation of Large Language Models with An Anonymous Crowd-Sourcing Platform
https://github.com/mingyuj666/stockagent
공식 코드https://github.com/mininglamp-mllm/hmllm
공식 코드https://github.com/miralab-ustc/gcn4kgc
공식 코드Rethinking Graph Convolutional Networks in Knowledge Graph Completion
https://github.com/mirunabetianu/dallmi
공식 코드DALLMi: Domain Adaption for LLM-based Multi-label Classifier
https://github.com/mistyreed63849/graph-llm
공식 코드GraphLLM: Boosting Graph Reasoning Ability of Large Language Model
https://github.com/mit-han-lab/omniserve
공식 코드QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
https://github.com/mit-han-lab/qserve
공식 코드QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
https://github.com/mit-ll/linkq
공식 코드LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering
https://github.com/mitmedialab/ai-false-memories
공식 코드Conversational AI Powered by Large Language Models Amplifies False Memories in Witness Interviews
https://github.com/mitsuichen14/lrsclip
공식 코드LRSCLIP: A Vision-Language Foundation Model for Aligning Remote Sensing Image with Longer Text
https://github.com/miulab/lmrec-survey
공식 코드Augment or Not? A Comparative Study of Pure and Augmented Large Language Model Recommenders
https://github.com/miulab/taiwan-llama
공식 코드Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
https://github.com/miulab/taiwan-llm
공식 코드Taiwan LLM: Bridging the Linguistic Divide with a Culturally Aligned Language Model
https://github.com/mj-storytelling/DiversityTuning
공식 코드Modifying Large Language Model Post-Training for Diverse Creative Writing
https://github.com/mkandai/llms-in-the-loop
공식 코드https://github.com/ml-ailab/ophglm
공식 코드https://github.com/mlfoundations/rtfm
공식 코드Large Scale Transfer Learning for Tabular Data via Language Modeling
https://github.com/mlfoundations/tabliblib
공식 코드Large Scale Transfer Learning for Tabular Data via Language Modeling
https://github.com/mlgroupjlu/matrixnuclearnorm
공식 코드https://github.com/mllm-tool/mllm-tool
공식 코드MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning
https://github.com/mllmkcbench/mllmkc
공식 코드Benchmarking Multimodal Knowledge Conflict for Large Multimodal Models
https://github.com/mlplyler/ICDA
공식 코드https://github.com/mlvlab/RALF
공식 코드https://github.com/mlvlab/llamo
공식 코드https://github.com/mmurrad/tweet-data-classification
공식 코드Multi-Lingual Cyber Threat Detection in Tweets/X Using ML, DL, and LLM: A Comparative Analysis
https://github.com/mnoukhov/async_rlhf
공식 코드Asynchronous RLHF: Faster and More Efficient Off-Policy RL for Language Models
https://github.com/mobs-fbk/llm_interaction_simulator
공식 코드https://github.com/modeeric/orbit-llama
공식 코드https://github.com/modelscope/mcpbench
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.