# 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.

## Libraries

Use these libraries to find Relational Reasoning models and implementations## Datasets

## 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

Relational reasoning is a central component of generally intelligent behavior, but has proven difficult for neural networks to learn.

# Graph-Based Global Reasoning Networks

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

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

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.