https://bitbucket.org/sdumancic/embeddingsmeetilp
공식 코드A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
Papers with Code 아카이브
Relational Reasoning
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The goal of Relational Reasoning is to figure out the relationships among different entities, such as image pixels, words or sentences, human skeletons or interactive moving agents.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/AKSW/Mandolin
공식 코드Mandolin: A Knowledge Discovery Framework for the Web of Data
https://github.com/Alibaba-NLP/ProtoRE
공식 코드Prototypical Representation Learning for Relation Extraction
https://github.com/AnselCmy/MetaR
공식 코드Meta Relational Learning for Few-Shot Link Prediction in Knowledge Graphs
https://github.com/CUVL/SSLR
공식 코드https://github.com/Cadene/murel.bootstrap.pytorch
공식 코드MUREL: Multimodal Relational Reasoning for Visual Question Answering
https://github.com/ChiCheng123/JointDet
공식 코드https://github.com/Cogito2012/CarCrashDataset
공식 코드Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning
https://github.com/Cogito2012/UString
공식 코드Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning
https://github.com/GXYM/DRRG
공식 코드Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection
https://github.com/GuillermoPuebla/RelationReasonNN
공식 코드https://github.com/GuillermoPuebla/object-centric-reasoning
공식 코드Visual Reasoning in Object-Centric Deep Neural Networks: A Comparative Cognition Approach
https://github.com/GustikS/GNNwLRNNs
공식 코드Beyond Graph Neural Networks with Lifted Relational Neural Networks
https://github.com/GustikS/NeuraLogic
공식 코드https://github.com/HaochenLiu2000/SAFER
공식 코드Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation
https://github.com/Hosein47/IEA-GAN
공식 코드https://github.com/IBM/RuDaS
공식 코드RuDaS: Synthetic Datasets for Rule Learning and Evaluation Tools
https://github.com/INK-USC/MHGRN
공식 코드Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering
https://github.com/Junjue-Wang/EarthVQA
공식 코드https://github.com/Kyubyong/sudoku
공식 코드https://github.com/Li-Wanhua/GR2N
공식 코드https://github.com/MM-IR/DualVGR-VideoQA
공식 코드DualVGR: A Dual-Visual Graph Reasoning Unit for Video Question Answering
https://github.com/MangoKiller/MolTC
공식 코드MolTC: Towards Molecular Relational Modeling In Language Models
https://github.com/MaryZolfaghar/CSLS
공식 코드Complementary Structure-Learning Neural Networks for Relational Reasoning
https://github.com/Mehran-k/RelNN
공식 코드https://github.com/Namkyeong/3DMRL
공식 코드3D Interaction Geometric Pre-training for Molecular Relational Learning
https://github.com/OreoChocolate/MUREN
공식 코드Relational Context Learning for Human-Object Interaction Detection
https://github.com/Panda0406/Zero-shot-knowledge-graph-relational-learning
공식 코드Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs
https://github.com/Sean-Bin-Yang/LightPath
공식 코드LightPath: Lightweight and Scalable Path Representation Learning
https://github.com/SkBlaz/PropStar
공식 코드Propositionalization and Embeddings: Two Sides of the Same Coin
https://github.com/StanleySun233/maritime-vts
공식 코드https://github.com/Steven51516/FlexMol
공식 코드FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
https://github.com/THUDM/CogKR
공식 코드Cognitive Knowledge Graph Reasoning for One-shot Relational Learning
https://github.com/TomHopeLab/ScicoRadar
공식 코드https://github.com/abril4416/Mod-Zero-VQA
공식 코드https://github.com/alexatartaglini/relational-circuits
공식 코드Beyond the Doors of Perception: Vision Transformers Represent Relations Between Objects
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.