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Found 7 papers, 4 papers with code
Federated Behavioural Planes: Explaining the Evolution of Client Behaviour in Federated Learning
Federated Learning (FL), a privacy-aware approach in distributed deep learning environments, enables many clients to collaboratively train a model without sharing sensitive data, thereby reducing privacy risks.
Categorical Foundations of Explainable AI: A Unifying Theory
Explainable AI (XAI) aims to address the human need for safe and reliable AI systems.
Interpretable Neural-Symbolic Concept Reasoning
Deep learning methods are highly accurate, yet their opaque decision process prevents them from earning full human trust.
Direct Comparative Analysis of Nature-inspired Optimization Algorithms on Community Detection Problem in Social Networks
Nature-inspired optimization Algorithms (NIOAs) are nowadays a popular choice for community detection in social networks.
Modeling Generalization in Machine Learning: A Methodological and Computational Study
As machine learning becomes more and more available to the general public, theoretical questions are turning into pressing practical issues.
A Novel Outlook on Feature Selection as a Multi-objective Problem
We thus propose a multi-objective optimization approach to feature selection, EvoFS, with the objectives to i. minimize feature subset size, ii.
Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms
A coreset is a subset of the training set, using which a machine learning algorithm obtains performances similar to what it would deliver if trained over the whole original data.
Ranked #1 on
Core set discovery
on MNIST
