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Learning in Convolutional Neural Networks Accelerated by Transfer Entropy
According to our experiments on CNN classifiers, to achieve a reasonable computational overhead--accuracy trade-off, it is efficient to consider only the inter-neural information transfer of a random subset of the neuron pairs from the last two fully connected layers.
Visual Knowledge Discovery with Artificial Intelligence: Challenges and Future Directions
This volume is devoted to the emerging field of Integrated Visual Knowledge Discovery that combines advances in Artificial Intelligence/Machine Learning (AI/ML) and Visualization/Visual Analytics.
Visualizing and Explaining Language Models
During the last decade, Natural Language Processing has become, after Computer Vision, the second field of Artificial Intelligence that was massively changed by the advent of Deep Learning.
Context-Sensitive Visualization of Deep Learning Natural Language Processing Models
The modified texts that produce the largest difference in the target classification output neuron are selected, and the combination of removed words are then considered to be the most influential on the model's output.
Learning in Feedforward Neural Networks Accelerated by Transfer Entropy
Our contribution is an information-theoretical method for analyzing information transfer between the nodes of feedforward neural networks.
