avenge
发表于 2025-3-23 11:13:22
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duplicate
发表于 2025-3-23 17:40:08
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
Bouquet
发表于 2025-3-23 19:35:31
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
分开
发表于 2025-3-23 23:21:04
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
粗俗人
发表于 2025-3-24 03:22:27
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.
cloture
发表于 2025-3-24 10:26:41
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积云
发表于 2025-3-24 11:44:10
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
micronized
发表于 2025-3-24 18:36:11
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字谜游戏
发表于 2025-3-24 19:26:56
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
Hamper
发表于 2025-3-25 03:08:33
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