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

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Prediction of?Reaching Movements with?Target Information Towards Trans-humeral Prosthesis Control Usjoints need to be reconstructed, and the less kinetic information is available in the residual limb. By exploiting contextual information, such as the position and orientation of a target in a reaching task, we aim to reconstruct the natural dynamics of the distal joints using recurrent neural netwo

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MADE: A Universal Fine-Tuning Framework to?Enhance Robustness of?Machine Reading Comprehension. However, they suffer from poor generalization ability and appear vulnerable facing even trivial attacks. We propose a novel MADE framework for automatically detecting potential biases in MRC models. Furthermore, by employing a three-stage enhanced fine-tuning method, we relieve the susceptibility

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Michael Giretzlehner,Lars-Peter Kamolzodel which uses a Graph Convolutional Network (GCN) to predict data projections on GTM, solely based on the information about the reaction and BB sets used for the library preparation. Ten DNA-Encoded Combinatorial Libraries (DELs), each containing one million compounds, were used to train and evalu

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Religion, Culture, and Business Legitimacyeak intensities. XGBoost classifiers were trained to correlate these spectroscopic signature matrices with molecular substructures represented as MACCS keys. We evaluated the model performance on the full dataset and on constrained chemical space subset. The results indicated that the model’s capaci

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