https://github.com/jingsonglv/CFG
Open Graph Benchmark: Datasets for Machine Learning on Graphs
Papers with Code 아카이브
Inductive knowledge graph completion
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/joe-prog/https-github.com-facebookresearch-ParlAI
https://github.com/joez17/chatbridge
ChatBridge: Bridging Modalities with Large Language Model as a Language Catalyst
https://github.com/johannesmessner/boxte
https://github.com/jonjoncardoso/jonjoncardoso
Very Large Language Model as a Unified Methodology of Text Mining
https://github.com/jquesnelle/scaled-rope
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
https://github.com/jquesnelle/yarn
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
https://github.com/julessanchez/x-nesyl
https://github.com/jzhang38/EasyContext
https://github.com/kaistai/semiparametric_token-sequence_co-supervision
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
https://github.com/kakaobrain/honeybee
MME: A Comprehensive Evaluation Benchmark for Multimodal Large Language Models
https://github.com/kamanphoebe/look-into-moes
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models
https://github.com/kamigaito/icml2022
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/kdexd/lang-emerge-parlai
https://github.com/kevin666aa/flaml
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference
https://github.com/kevinwu23/SourceCheckup
How well do LLMs cite relevant medical references? An evaluation framework and analyses
https://github.com/keyhsw/chemllm
https://github.com/khalil-research/1d-arc
https://github.com/kingcong/models/tree/main/hake
Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Prediction
https://github.com/kinit-sk/l3d-sensitivity-investigation
https://github.com/knotgrass/attention/blob/main/attn/attention.py
https://github.com/knowlab/bi-weekly-paper-presentation
Understanding the Performance of Knowledge Graph Embeddings in Drug Discovery
https://github.com/kracr/document-level-relation-extraction
https://github.com/krantirk/CycleGT
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
https://github.com/kuk/rulm-sbs2
https://github.com/kyegomez/PALM-E
https://github.com/kyutai-labs/hibiki
Moshi: a speech-text foundation model for real-time dialogue
https://github.com/kyutai-labs/moshivis
Moshi: a speech-text foundation model for real-time dialogue
https://github.com/labbeti/aac-datasets
https://github.com/lamm-mit/pdf2audio
SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
https://github.com/lars-research/3d-pgt
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs
https://github.com/lars-research/red-gnn
https://github.com/lastmile-ai/aiconfig
YUAN 2.0: A Large Language Model with Localized Filtering-based Attention
https://github.com/laurendelong21/mars
Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
https://github.com/layumi/University1652-Baseline
Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching
https://github.com/leloykun/mmfm-challenge
MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.