Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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Enhancing Counterfactual Image Generation Using Mahalanobis Distance with?Distribution Preferences iage counterfactual explanations. Our experiments demonstrate that the counterfactual explanations generated by our method closely resemble the original images in both pixel and feature spaces. Additionally, our method outperforms established baselines, achieving impressive experimental results.

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Exploring Task-Specific Dimensions in?Word Embeddings Through Automatic Rule Learningd the other for gender classification and sentiment analysis. Notably, the results reveal that the removal of gender-related dimensions significantly affects gender classification performance while having minimal impact on other tasks. This highlights that there exist different related dimensions fo

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Andrei N. Borodin,Paavo Salminentation influence the formation of category-specific representations. This allows us not only to better understand the principles behind MIM, but to then reassemble a MIM more in line with the focused nature of biological perception. We find that MIM disentangles neurons in latent space without expli

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Sherri Sharp,Walter J. Meyer III M.D.e of PixelCNNs, as well as constraints in image resolution and complexity. In this work, we address these limitations and further investigate how attentional selection affects memory accuracy and generativity. First, we substitute the PixelCNN with a Transformer model (semantic memory) to capture un