About

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

Benchmarks

TREND DATASET BEST METHOD PAPER TITLE PAPER CODE COMPARE

Datasets

Greatest papers with code

Fast Graph Representation Learning with PyTorch Geometric

6 Mar 2019rusty1s/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.

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION RELATIONAL REASONING

Relational inductive biases, deep learning, and graph networks

4 Jun 2018deepmind/graph_nets

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.

DECISION MAKING RELATIONAL REASONING

Complex Embeddings for Simple Link Prediction

20 Jun 2016stellargraph/stellargraph

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

LINK PREDICTION RELATIONAL REASONING

Knowledge Graph Completion via Complex Tensor Factorization

22 Feb 2017Accenture/AmpliGraph

In statistical relational learning, knowledge graph completion deals with automatically understanding the structure of large knowledge graphs---labeled directed graphs---and predicting missing relationships---labeled edges.

KNOWLEDGE GRAPH COMPLETION LINK PREDICTION RELATIONAL REASONING

Holographic Embeddings of Knowledge Graphs

16 Oct 2015Accenture/AmpliGraph

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

KNOWLEDGE GRAPHS LINK PREDICTION RELATIONAL REASONING

A Survey on Knowledge Graphs: Representation, Acquisition and Applications

2 Feb 2020shaoxiongji/awesome-knowledge-graph

In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.

4 KNOWLEDGE GRAPH COMPLETION KNOWLEDGE GRAPH EMBEDDING RELATIONAL REASONING

Recurrent Relational Networks for complex relational reasoning

ICLR 2018 Kyubyong/sudoku

Humans possess an ability to abstractly reason about objects and their interactions, an ability not shared with state-of-the-art deep learning models.

RELATIONAL REASONING

Recurrent Relational Networks

NeurIPS 2018 Kyubyong/sudoku

We achieve state of the art results on the bAbI textual question-answering dataset with the recurrent relational network, consistently solving 20/20 tasks.

Ranked #3 on Question Answering on bAbi (Mean Error Rate metric)

QUESTION ANSWERING RELATIONAL REASONING

Temporal Relational Reasoning in Videos

ECCV 2018 zhoubolei/TRN-pytorch

Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species.

ACTION CLASSIFICATION ACTION RECOGNITION COMMON SENSE REASONING HUMAN-OBJECT INTERACTION DETECTION RELATIONAL REASONING