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Found 8 papers, 1 papers with code
Is Attention Explanation? An Introduction to the Debate
In this paper, we provide a clear overview of the insights on the debate by critically confronting works from these different areas.
L’Attention est-elle de l’Explication ? Une Introduction au Débat (Is Attention Explanation ? An Introduction to the Debate )
Nous présentons un résumé en français et un résumé en anglais de l’article Is Attention Explanation ?
SO(2) and O(2) Equivariance in Image Recognition with Bessel-Convolutional Neural Networks
For many years, it has been shown how much exploiting equivariances can be beneficial when solving image analysis tasks.
Achieving Rotational Invariance with Bessel-Convolutional Neural Networks
For many applications in image analysis, learning models that are invariant to translations and rotations is paramount.
DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality
A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional parametrization (e. g. t-SNE, UMAP, etc.).
Impact of Legal Requirements on Explainability in Machine Learning
The requirements on explainability imposed by European laws and their implications for machine learning (ML) models are not always clear.
ML + FV = $\heartsuit$? A Survey on the Application of Machine Learning to Formal Verification
Formal Verification (FV) and Machine Learning (ML) can seem incompatible due to their opposite mathematical foundations and their use in real-life problems: FV mostly relies on discrete mathematics and aims at ensuring correctness; ML often relies on probabilistic models and consists of learning patterns from training data.
Learning Interpretability for Visualizations using Adapted Cox Models through a User Experiment
In order to be useful, visualizations need to be interpretable.
