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Found 7 papers, 2 papers with code
A Comprehensive Survey on Integrating Large Language Models with Knowledge-Based Methods
This approach aims to combine the generative language understanding of LLMs and the precise knowledge representation systems by which they are integrated.
Defining Reference Sequences for Nocardia Species by Similarity and Clustering Analyses of 16S rRNA Gene Sequence Data
The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods.
A Model for Intelligible Interaction Between Agents That Predict and Explain
Machine Learning (ML) has emerged as a powerful form of data modelling with widespread applicability beyond its roots in the design of autonomous agents.
One-way Explainability Isn't The Message
We propose operational principles -- we call them Intelligibility Axioms -- to guide the design of a collaborative decision-support system.
Logical Explanations for Deep Relational Machines Using Relevance Information
We investigate the use of a Bayes-like approach to identify logical proxies for local predictions of a DRM.
B-CNN: Branch Convolutional Neural Network for Hierarchical Classification
In this way we show that CNN based models can be forced to learn successively coarse to fine concepts in the internal layers at the output stage, and that hierarchical prior knowledge can be adopted to boost CNN models' classification performance.
Word Vector Enrichment of Low Frequency Words in the Bag-of-Words Model for Short Text Multi-class Classification Problems
The bag-of-words model is a standard representation of text for many linear classifier learners.
