Relational Reasoning

111 papers with code • 1 benchmarks • 11 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.

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

Libraries

Use these libraries to find Relational Reasoning models and implementations

Most implemented papers

Relational inductive biases, deep learning, and graph networks

deepmind/graph_nets 4 Jun 2018

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 NeurIPS 2017

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

Complex Embeddings for Simple Link Prediction

ttrouill/complex 20 Jun 2016

In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases.

Relational Deep Reinforcement Learning

inoryy/reaver 5 Jun 2018

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.

Inductive Relation Prediction by Subgraph Reasoning

kkteru/grail ICML 2020

The dominant paradigm for relation prediction in knowledge graphs involves learning and operating on latent representations (i. e., embeddings) of entities and relations.

Recurrent Relational Networks

Kyubyong/sudoku 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.

Graph-Based Global Reasoning Networks

facebookresearch/GloRe 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.

CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text

facebookresearch/clutrr IJCNLP 2019

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

Accenture/AmpliGraph 16 Oct 2015

Learning embeddings of entities and relations is an efficient and versatile method to perform machine learning on relational data such as knowledge graphs.

Fast Graph Representation Learning with PyTorch Geometric

rusty1s/pytorch_geometric 6 Mar 2019

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.