Titlebook: Neural Information Processing; 29th International C Mohammad Tanveer,Sonali Agarwal,Adam Jatowt Conference proceedings 2023 The Editor(s) (

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Conference proceedings 2023cessing, ICONIP 2022, held as a virtual event, November 22–26, 2022.?.The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computin

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GCD-PKAug: A Gradient Consistency Discriminator-Based Augmentation Method for?Pharmacokinetics Time uch as precision dosing. However, small sample size makes learning-based PK prediction a challenging task. This paper introduces Gradient Consistency Discriminator-based PK Augmentation (.), which is a novel data augmentation method tailored for PK time courses. Gradient consistency is calculated ba

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ISP-FESAN: Improving Significant Wave Height Prediction with?Feature Engineering and?Self-attention r, it is challenging to accurately forecast ocean waves due to their non-linear and non-smooth characteristics. To overcome this difficulty, we propose the ISP-FESAN method, which optimizes significant wave height prediction by feature engineering and self-attention networks. Specifically, in the pr

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Binary Orthogonal Non-negative Matrix Factorizationon several representative real-world data sets. The numerical results confirm that the method has improved accuracy compared to the related techniques. The proposed method is fast for training and classification and space efficient.

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Interpretable Decision Tree Ensemble Learning with?Abstract Argumentation for?Binary Classificationes to produce better predictive performance and intrinsically interpretable than state-of-the-art ensemble models. Our approach called . is a self-explainable model that first learns a group of decision trees from a given dataset. It then treats all decision trees as knowledgable agents and let them

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Adaptive Rounding Compensation for?Post-training Quantizationan be deployed to resource-limited devices. Post-Training Quantization (PTQ) is a practical method of generating a hardware-friendly quantized network without re-training or fine-tuning. However, PTQ results in unacceptable accuracy degradation due to disturbance caused by clipping and discarding th

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