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Found 12 papers, 0 papers with code
Towards a general framework for improving the performance of classifiers using XAI methods
Modern Artificial Intelligence (AI) systems, especially Deep Learning (DL) models, poses challenges in understanding their inner workings by AI researchers.
Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning
While this approach provides convenience, it raises concerns about the reliability of outcomes, leading to challenges such as incorrect performance evaluation.
Strategies to exploit XAI to improve classification systems
Explainable Artificial Intelligence (XAI) aims to provide insights into the decision-making process of AI models, allowing users to understand their results beyond their decisions.
Hidden Classification Layers: Enhancing linear separability between classes in neural networks layers
To this aim, we propose a neural network architecture which induces an error function involving the outputs of all the network layers.
Machine Learning Strategies to Improve Generalization in EEG-based Emotion Assessment: \\a Systematic Review
Several architectures and methods have been proposed to address this issue, mainly based on transfer learning methods.
Toward the application of XAI methods in EEG-based systems
An interesting case of the well-known Dataset Shift Problem is the classification of Electroencephalogram (EEG) signals in the context of Brain-Computer Interface (BCI).
On The Effects Of Data Normalisation For Domain Adaptation On EEG Data
This paper focuses on the impact of data normalisation, or standardisation strategies applied together with DA methods.
Exploiting auto-encoders and segmentation methods for middle-level explanations of image classification systems
We start from the hypothesis that some autoencoders, relying on standard data representation approaches, could extract more salient and understandable input properties, which we call here \textit{Middle-Level input Features} (MLFs), for a user with respect to raw low-level features.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
Adaptive Filters in Graph Convolutional Neural Networks
Over the last few years, we have witnessed the availability of an increasing data generated from non-Euclidean domains, which are usually represented as graphs with complex relationships, and Graph Neural Networks (GNN) have gained a high interest because of their potential in processing graph-structured data.
A general approach to compute the relevance of middle-level input features
This work proposes a novel general framework, in the context of eXplainable Artificial Intelligence (XAI), to construct explanations for the behaviour of Machine Learning (ML) models in terms of middle-level features.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
+1
A survey on modern trainable activation functions
In neural networks literature, there is a strong interest in identifying and defining activation functions which can improve neural network performance.
A simple and efficient architecture for trainable activation functions
Learning automatically the best activation function for the task is an active topic in neural network research.
