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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.

Source: Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/moduIo/Relation-Networks

A simple neural network module for relational reasoning

https://github.com/mylonasc/tf-gnns

Relational inductive biases, deep learning, and graph networks

https://github.com/nathangrinsztajn/Box-World

Relational Deep Reinforcement Learning

https://github.com/nathanin/pad

Relational Deep Reinforcement Learning

https://github.com/ncfrey/litmatter

Fast Graph Representation Learning with PyTorch Geometric

https://github.com/nerdimite/relation-network

A simple neural network module for relational reasoning

https://github.com/nicoladainese96/RelationalDeepRL

Relational Deep Reinforcement Learning

https://github.com/nicoladainese96/SC2-RL

Relational Deep Reinforcement Learning

https://github.com/nrel/m2p

Relational inductive biases, deep learning, and graph networks

https://github.com/okankop/MFF-pytorch

Temporal Relational Reasoning in Videos

https://github.com/open-mmlab/mmocr

Deep Relational Reasoning Graph Network for Arbitrary Shape Text Detection

https://github.com/patrickhart/jaxdl

Relational inductive biases, deep learning, and graph networks

https://github.com/pnnl/expert2

Anticipating Technical Expertise and Capability Evolution in Research Communities using Dynamic Graph Transformers

https://github.com/priyeshshukla/graph_nets_ids576

Relational inductive biases, deep learning, and graph networks

https://github.com/pyg-team/pytorch_geometric/blob/2947af7733d87315a9218a0bfabbfda10b595f0e/torch_geometric/nn/kge/complex.py

Complex Embeddings for Simple Link Prediction

https://github.com/pykeen/pykeen

Complex Embeddings for Simple Link Prediction

https://github.com/raphaelavalos/attention_tsp_graph_net

Relational inductive biases, deep learning, and graph networks

https://github.com/rickyHong/Graph-Net-repl-for-Jigsaw

Relational inductive biases, deep learning, and graph networks

https://github.com/rishikmani/sort-of-clevr

A simple neural network module for relational reasoning

https://github.com/rmcong/RRNet_TGRS2021

RRNet: Relational Reasoning Network with Parallel Multi-scale Attention for Salient Object Detection in Optical Remote Sensing Images

https://github.com/ronghanghu/lcgn

Language-Conditioned Graph Networks for Relational Reasoning

https://github.com/ronuchit/LOFT_IROS_2021

Learning Symbolic Operators for Task and Motion Planning

https://github.com/rosinality/relation-networks-pytorch

A simple neural network module for relational reasoning

https://github.com/salvaRC/graphnet_pytorch

Relational inductive biases, deep learning, and graph networks

https://github.com/sbadredd/semantic-pascal-part

Logic Tensor Networks for Semantic Image Interpretation

https://github.com/scofield7419/DiaRE-D2G

Reasoning with Latent Structure Refinement for Document-Level Relation Extraction

https://github.com/siddk/relation-network

A simple neural network module for relational reasoning

https://github.com/sinbycos/FaceTrack

Robust Face Tracking using Multiple Appearance Models and Graph Relational Learning

https://github.com/sntcristian/and-kge

Complex Embeddings for Simple Link Prediction

https://github.com/stellargraph/stellargraph

Complex Embeddings for Simple Link Prediction

https://github.com/tajamul21/fate

Your Diffusion Model is Secretly a Zero-Shot Classifier

https://github.com/tgebhart/sheaf_kg_transind

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/thunlp/gp-gnn

Graph Neural Networks with Generated Parameters for Relation Extraction

https://github.com/tommasocarraro/ltntorch

Logic Tensor Networks

https://github.com/ttok0s7u2n5/ML2_proj

A simple neural network module for relational reasoning

https://github.com/uclmr/inferbeddings

Adversarial Sets for Regularising Neural Link Predictors

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.