Relational Reasoning
149 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
RAVEN
AbstractReasoning
PGM
PISC
SPARTQA -
Most implemented papers
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.
Generative 3D Part Assembly via Dynamic Graph Learning
hyperplane-lab/Generative-3D-Part-Assembly
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NeurIPS 2020
Analogous to buying an IKEA furniture, given a set of 3D parts that can assemble a single shape, an intelligent agent needs to perceive the 3D part geometry, reason to propose pose estimations for the input parts, and finally call robotic planning and control routines for actuation.
Learning from Protein Structure with Geometric Vector Perceptrons
drorlab/gvp-pytorch
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ICLR 2021
Learning on 3D structures of large biomolecules is emerging as a distinct area in machine learning, but there has yet to emerge a unifying network architecture that simultaneously leverages the graph-structured and geometric aspects of the problem domain.
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning
jyhong0304/SII
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We demonstrate that compared to LTNs, RWFNs can achieve better or similar performance for both object classification and detection of the part-of relations between objects in SII tasks while using much far fewer learnable parameters (1:62 ratio) and a faster learning process (1:2 ratio of running speed).
RLIPv2: Fast Scaling of Relational Language-Image Pre-training
jacobyuan7/rlipv2
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ICCV 2023
In this paper, we propose RLIPv2, a fast converging model that enables the scaling of relational pre-training to large-scale pseudo-labelled scene graph data.
Knowledge Graph Completion via Complex Tensor Factorization
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.
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings.
Relational recurrent neural networks
L0SG/relational-rnn-pytorch
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NeurIPS 2018
Memory-based neural networks model temporal data by leveraging an ability to remember information for long periods.
Mapping Natural Language Commands to Web Elements
stanfordnlp/phrasenode
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EMNLP 2018
The web provides a rich, open-domain environment with textual, structural, and spatial properties.
Compositional Language Understanding with Text-based Relational Reasoning
Neural networks for natural language reasoning have largely focused on extractive, fact-based question-answering (QA) and common-sense inference.
