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Found 8 papers, 1 papers with code
Feature extraction using Spectral Clustering for Gene Function Prediction using Hierarchical Multi-label Classification
Gene annotation addresses the problem of predicting unknown associations between gene and functions (e. g., biological processes) of a specific organism.
Targeted transfer learning to improve performance in small medical physics datasets
The growing use of Machine Learning has produced significant advances in many fields.
Spectral Evolution with Approximated Eigenvalue Trajectories for Link Prediction
The spectral evolution model aims to characterize the growth of large networks (i. e., how they evolve as new edges are established) in terms of the eigenvalue decomposition of the adjacency matrices.
A Top-down Supervised Learning Approach to Hierarchical Multi-label Classification in Networks
Node classification is the task of inferring or predicting missing node attributes from information available for other nodes in a network.
Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification
The availability of genomic data has grown exponentially in the last decade, mainly due to the development of new sequencing technologies.
On Computing Probabilistic Explanations for Decision Trees
Formal XAI (explainable AI) is a growing area that focuses on computing explanations with mathematical guarantees for the decisions made by ML models.
A neuro-symbolic framework for answering conjunctive queries
The problem of answering logical queries over incomplete knowledge graphs is receiving significant attention in the machine learning community.
The Distributional Uncertainty of the SHAP score in Explainable Machine Learning
In this paper, we propose a principled framework for reasoning on SHAP scores under unknown entity population distributions.
