Relational Reasoning
141 papers with code • 1 benchmarks • 12 datasets
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.
Benchmarks
These leaderboards are used to track progress in Relational Reasoning
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Libraries
Use these libraries to find Relational Reasoning models and implementationsDatasets
WebKB
AbstractReasoning
RAVEN
PGM
PISC
SPARTQA -
Most implemented papers
Relational inductive biases, deep learning, and graph networks
As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.
A simple neural network module for relational reasoning
kimhc6028/relational-networks
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NeurIPS 2017
Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn.
Graph-Based Global Reasoning Networks
facebookresearch/GloRe
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CVPR 2019
In this work, we propose a new approach for reasoning globally in which a set of features are globally aggregated over the coordinate space and then projected to an interaction space where relational reasoning can be efficiently computed.
Complex Embeddings for Simple Link Prediction
In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases.
Inductive Relation Prediction by Subgraph Reasoning
The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.
Relational Deep Reinforcement Learning
inoryy/reaver
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We introduce an approach for deep reinforcement learning (RL) that improves upon the efficiency, generalization capacity, and interpretability of conventional approaches through structured perception and relational reasoning.
Recurrent Relational Networks
Kyubyong/sudoku
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NeurIPS 2018
We achieve state of the art results on the bAbI textual question-answering dataset with the recurrent relational network, consistently solving 20/20 tasks.
Fast Graph Representation Learning with PyTorch Geometric
We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.
CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text
The recent success of natural language understanding (NLU) systems has been troubled by results highlighting the failure of these models to generalize in a systematic and robust way.
Holographic Embeddings of Knowledge Graphs
Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.
