https://github.com/xingrun-xing/spikelm
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
Papers with Code 아카이브
Knowledge Graphs
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/xiongsiheng/TEILP
TEILP: Time Prediction over Knowledge Graphs via Logical Reasoning
https://github.com/xiuyu0000/papers_with_examples/tree/main/vogue
https://github.com/xmubq/dtr-text
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data
https://github.com/xyznlp/trans
Open Graph Benchmark: Datasets for Machine Learning on Graphs
https://github.com/yakazimir/semantic_fragments
What Does My QA Model Know? Devising Controlled Probes using Expert Knowledge
https://github.com/yang3121099/LLM-Neo
LLM-Neo: Parameter Efficient Knowledge Distillation for Large Language Models
https://github.com/yangletliu/finlora
https://github.com/yangyijune/mma-diffusion
GuardT2I: Defending Text-to-Image Models from Adversarial Prompts
https://github.com/yangyucheng000/Papers/tree/main/MindSpore-Retrieval-augmented
https://github.com/yangyucheng000/University/tree/main/model-1/rag
RAGA: Relation-aware Graph Attention Networks for Global Entity Alignment
https://github.com/yangyucheng000/University/tree/main/model-1/realm
https://github.com/yangyucheng000/University/tree/main/model-3/rag
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
https://github.com/yangyucheng000/University/tree/main/model-3/realm
https://github.com/yangyucheng000/VOGUE
https://github.com/yangyucheng000/multi-basis-encoding
Inductive Knowledge Graph Reasoning for Multi-batch Emerging Entities
https://github.com/yangzhengyi98/dreamrec
Large Language Model Can Interpret Latent Space of Sequential Recommender
https://github.com/yaof20/real
https://github.com/yeounoh/lc_nl2sql
https://github.com/yhzhu99/medagentboard
https://github.com/ymoslem/opennmt-tutorial
Fine-tuning Large Language Models for Adaptive Machine Translation
https://github.com/youngsheen/gpst
Talk With Human-like Agents: Empathetic Dialogue Through Perceptible Acoustic Reception and Reaction
https://github.com/ytbai/ogbn_arxiv_dgl
Open Graph Benchmark: Datasets for Machine Learning on Graphs
https://github.com/yu-yang-li/starglm
Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification
https://github.com/yuleiniu/rva
https://github.com/yuxiaw/openfactcheck
RARR: Researching and Revising What Language Models Say, Using Language Models
https://github.com/yuyuz/Variational-Reasoning-Networks
Variational Reasoning for Question Answering with Knowledge Graph
https://github.com/z-qiyu/KGNN-LS
Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
https://github.com/zebangcheng/emotion-llama
MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
https://github.com/zehong-wang/gft
https://github.com/zetangforward/lcm_stack
L-CiteEval: Do Long-Context Models Truly Leverage Context for Responding?
https://github.com/zhaoolee/garss
https://github.com/zhengli97/promptkd
https://github.com/zhengxujosh/awesome-rag-vision
Retrieval Augmented Generation and Understanding in Vision: A Survey and New Outlook
https://github.com/zhenqincn/fedkseed
https://github.com/zhongpeixiang/ckc
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.