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Tag

#classification

5 articles tagged #classification

arxivMay 21bullish

INSHAPE: Instance-Level Shapelets for Interpretable Time-Series Classification

arXiv:2605.20088v1 Announce Type: cross Abstract: Discovering shapelets -- i.e., discriminative temporal patterns within time series -- has been widely studied to address the inherent complexity of time-series classification (TSC) and to make model decision-making processes more transparent. However

IN1 model#time-series#classification#interpretabilityRead on arxiv →
arxivMay 19bullish

UniAlign: A Model-Agnostic Framework for Robust Network Traffic Classification under Distribution Shifts

arXiv:2605.17575v1 Announce Type: cross Abstract: Network traffic classification (NTC) models often suffer severe performance degradation when deployed in real-world environments due to distribution shifts caused by changing network conditions. Existing robustness-enhancing approaches are commonly c

#network-traffic#classification#robustnessRead on arxiv →
arxivMay 16

A Two-Dimensional Framework for AI Agent Design Patterns: Cognitive Function and Execution Topology

arXiv:2605.13850v1 Announce Type: new Abstract: Existing frameworks for LLM-based agent architectures describe systems from a single perspective: industry guides (Anthropic, Google, LangChain) focus on execution topology -- how data flows -- while cognitive science surveys focus on cognitive functio

#architecture#framework#classificationRead on arxiv →
arxivMay 8bullish

Benchmarking PNW Model for MedMNIST to 100% Accuracy

arXiv:2604.18916v4 Announce Type: replace Abstract: In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be trained error-free, thus acquiring the capability of not making repeated mistakes. The method is

#machine-learning#classification#biomedicalRead on arxiv →
arxivApr 24bullish

Energy-Based Open-Set Active Learning for Object Classification

arXiv:2604.20083v1 Announce Type: new Abstract: Active learning (AL) has emerged as a crucial methodology for minimizing labeling costs in deep learning by selecting the most valuable samples from a pool of unlabeled data for annotation. Traditional AL operates under a closed-set assumption, where a

ENEN2 models#active-learning#open-set#classificationRead on arxiv →
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