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Relational Reasoning

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보존된 평가 기록: 1

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/Rowing0914/Graph_Nets

Relational inductive biases, deep learning, and graph networks

https://github.com/SamsungSAILMontreal/ForestDiffusion

Your Diffusion Model is Secretly a Zero-Shot Classifier

https://github.com/TQCAI/graph_nets_pytorch

Relational inductive biases, deep learning, and graph networks

https://github.com/WeiKangda/clutrr

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

https://github.com/adriangoe/relational-networks-pytorch

A simple neural network module for relational reasoning

https://github.com/allenai/CommonGen-Eval

CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning

https://github.com/amaleki2/graph_networks

Relational inductive biases, deep learning, and graph networks

https://github.com/anskarl/LoMRF

Semi-Supervised Online Structure Learning for Composite Event Recognition

https://github.com/aravic/graph_nets

Relational inductive biases, deep learning, and graph networks

https://github.com/ashutosh1919/neuro-symbolic-sudoku-solver

Neural Logic Machines

https://github.com/automl-research/red-gnn

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/awni00/abstract_transformer

Disentangling and Integrating Relational and Sensory Information in Transformer Architectures

https://github.com/awni00/relational-neural-networks

An Explicitly Relational Neural Network Architecture

https://github.com/bi-graph/emgraph

Holographic Embeddings of Knowledge Graphs

https://github.com/canlinzhang/siailp

Inductive Relation Prediction by Subgraph Reasoning

https://github.com/cheezzjazz/graph_nets

Relational inductive biases, deep learning, and graph networks

https://github.com/cheonbok94/Pytorch-Relational-Recurrent-Neural-networks

Relational recurrent neural networks

https://github.com/clvrai/relation-network-tensorflow

A simple neural network module for relational reasoning

https://github.com/cnichkawde/MatchingNetwork

A simple neural network module for relational reasoning

https://github.com/davidtangGT/MEGNET

Relational inductive biases, deep learning, and graph networks

https://github.com/dayanayuan/raa-kgc

Complex Embeddings for Simple Link Prediction

https://github.com/dcccc/LC_NET

Relational inductive biases, deep learning, and graph networks

https://github.com/dcccc/git_python

Relational inductive biases, deep learning, and graph networks

https://github.com/deepmind/jraph

Relational inductive biases, deep learning, and graph networks

https://github.com/dmeoli/neuro-sat

Relational inductive biases, deep learning, and graph networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/rrn

Recurrent Relational Networks

https://github.com/drorlab/gvp

Learning from Protein Structure with Geometric Vector Perceptrons

https://github.com/duanyzhi/graph_net

Relational inductive biases, deep learning, and graph networks

https://github.com/eezywu/PARN

PARN: Position-Aware Relation Networks for Few-Shot Learning

https://github.com/fcorencoret/dynamic-rn

A simple neural network module for relational reasoning

https://github.com/filco306/challenging-structural-assumptions

Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs

https://github.com/giancarlok/relationaltasks

An Explicitly Relational Neural Network Architecture

https://github.com/gitlimlab/Relation-Network-Tensorflow

A simple neural network module for relational reasoning

https://github.com/gngdb/relational

A simple neural network module for relational reasoning

https://github.com/google/neural-logic-machines

Neural Logic Machines

https://github.com/hcplab-sysu/causal-vlreasoning

Visual Causal Scene Refinement for Video Question Answering

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