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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

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發(fā)表于 2025-3-23 09:48:58 | 只看該作者
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發(fā)表于 2025-3-23 15:26:18 | 只看該作者
,PCB Component Rotation Detection Based on?Polarity Identifier Attention, integrated circuits, manually labeling large datasets of all kinds of components for training machine learning models is time-consuming and impractical. To address this challenge, we propose a novel one-shot conditional component rotation detection framework, PolarNet. Given a standard image of the
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發(fā)表于 2025-3-23 18:11:19 | 只看該作者
,PCDialogEval: Persona and?Context Aware Emotional Dialogue Evaluation,ffective evaluation metrics for emotional dialogue are in urgent need. However, most existing evaluation methods ignore the impact of users’ individual differences and situational contexts on emotional expressions, which poses a significant challenge in assessing the emotional expression capabilitie
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發(fā)表于 2025-3-24 01:02:47 | 只看該作者
,PlantDet: A Benchmark for?Plant Detection in the?Three-Rivers-Source Region,equirements of botanical research and intelligent plant management, we construct a dataset for .lant detection in the .hree-.iver-.ource region (PTRS). It comprises 21 types, 6965 high-resolution images of 2160 . 3840 pixels, captured by diverse sensors and platforms, and featuring objects of varyin
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發(fā)表于 2025-3-24 03:20:42 | 只看該作者
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發(fā)表于 2025-3-24 08:51:31 | 只看該作者
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發(fā)表于 2025-3-24 12:47:08 | 只看該作者
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發(fā)表于 2025-3-24 15:23:36 | 只看該作者
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發(fā)表于 2025-3-24 21:15:51 | 只看該作者
Pure Physics-Informed Echo State Network of ODE Solution Replicator,nary Differential Equations). By training with the differential equation itself, a pure physics-informed echo state network (ESN) is firstly introduced based on the loss function as consistent with the general invariance of differential equations, in which the actual training scheme is implemented a
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發(fā)表于 2025-3-25 02:09:17 | 只看該作者
Spannungskonzentrationen und Kerbwirkung,odel its own features in a single sample, it ignores the potential relevance of the whole dataset. Additionally, medical image datasets are typically small, making it crucial to obtain as many features as possible within a limited dataset. To address these problems, we propose the Multiple External
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