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Found 10 papers, 2 papers with code
CNN-based explanation ensembling for dataset, representation and explanations evaluation
Explainable Artificial Intelligence has gained significant attention due to the widespread use of complex deep learning models in high-stake domains such as medicine, finance, and autonomous cars.
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions
As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
A novel structured argumentation framework for improved explainability of classification tasks
The study presents a methodology for construction of $xADGs$ and evaluates their size and predictive capacity for classification tasks of varying magnitudes.
An investigation of the reconstruction capacity of stacked convolutional autoencoders for log-mel-spectrograms
In an unsupervised manner, the network is able to reconstruct a monophonic and harmonic sound based on latent representations.
Modeling cognitive load as a self-supervised brain rate with electroencephalography and deep learning
This research contributes to the body of knowledge by providing scholars with a novel computational method for mental workload modelling that aims to be generally applicable, does not rely on ad-hoc human-crafted models supporting replicability and falsifiability.
Comparing and extending the use of defeasible argumentation with quantitative data in real-world contexts
Computational trust was selected as the domain of application of such models.
Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review
Hence we advocate for a more methodical approach to the application of theories of human reasoning as well as the development of appropriate benchmarks, which we hope can lead to a better understanding of progress in the field.
An Evaluation of the EEG alpha-to-theta and theta-to-alpha band Ratios as Indexes of Mental Workload
This research contributes to the body of knowledge by demonstrating the richness of the information in the temporal, spectral and statistical domains extracted from the alpha-to-theta and theta-to-alpha EEG band ratios for the discrimination of self-reported perceptions of mental workload.
Explainable Artificial Intelligence: a Systematic Review
Explainable Artificial Intelligence (XAI) has experienced a significant growth over the last few years.
Pseudorehearsal in actor-critic agents with neural network function approximation
Catastrophic forgetting has a significant negative impact in reinforcement learning.
