srlearn-compatible relational datasets
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Updated
Nov 4, 2022 - Shell
srlearn-compatible relational datasets
Implementation of the framework in the paper: Waegeman, W., Pahikkala, T., Airola, A., Salakoski, T., Stock, M., & De Baets, B. (2012). A kernel-based framework for learning graded relations from data. IEEE Transactions on Fuzzy Systems, 20(6), 1090-1101.
A grammar and linter for ILP datasets.
Experimental setup and results for 2021-2022 academic research "Effects of knowledge graph structural properties on their predictive performance".
A code base for Automated Relational Feature Engineering
Lossless Compression of Structured Convolutional Models via Lifting
This is the project repo associated with the paper "Disentangling and Integrating Relational and Sensory Information in Transformer Architectures" by Awni Altabaa, John Lafferty
Julia package for fetching and using srlearn-compatible relational datasets.
Word embeddings for Transfer Learning using Relational Dependency Networks
A package for generating Relational Features for PDDL Planning
Implementation of a learning and fragment-based rule inference engine -- M. Svatoš, S. Schockaert, J. Davis, and O. Kuželka: STRiKE: Rule-driven relational learning using stratified k-entailment, ECAI'20
Computes contingency tables for relational databases, i.e. counts across tables
Code and data to the publication "SpikE: spike-based embeddings for multi-relational graph data".
Official implementation of "Relational Proxies: Emergent Relationships as Fine-Grained Discriminators", NeurIPS 2022.
Learning for planning architecture using both classical and deep learning methods.
Project repository for MA6040: Fuzzy Logic Connectives: Theory and Applications offered in Spring 2019
Beyond Graph Neural Networks with Lifted Relational Neural Networks
PyTorch implementation of the paper "NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning" (AAAI'24)
Python package for fetching and using srlearn-compatible relational datasets.
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