Hugging Face Trending Papers
Anomaly Detection on Resource-Constrained Devices with Reservoir Computing
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arXiv Machine Learning
Jul 15
Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices
arXiv:2607. 12599v1 Announce Type: new Abstract: Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption.
Hugging Face Trending Papers
Jul 14
Lightweight Multi-Scale Anomaly Detection for Resource-Constrained Edge Devices
Time-series anomaly detection is increasingly important in IoT systems, sensor networks, and edge monitoring applications, where models must operate under strict constraints on memory, latency, and power consumption. While recent deep-learning approaches have improved detection accuracy, many remain computationally expensive and often fail to capture subtle anomalies due to limited multi-scale sensitivity.
arXiv Machine Learning
Jun 15
D2H-AD: A Hybrid Model Utilizing Hyperdimensional Computing for Advanced Anomaly Detection
arXiv:2606. 13754v1 Announce Type: new Abstract: Anomaly detection is a fundamental component of intelligent systems with applications in healthcare, cybersecurity, smart grids, and IoT environments.
arXiv Machine Learning
Aug 5
Design-Time Optimization of Deep Neural Networks for Intermittent Learning on Microcontrollers
arXiv:2608. 03589v1 Announce Type: new Abstract: We present a method for designing deep neural networks (DNNs) for intermittent, energy-autonomous, on-device learning on microcontroller units (MCUs).
arXiv Machine Learning
Aug 4
CARE: A Cascaded Framework for Efficient and Reliable Time Series Anomaly Detection
arXiv:2608. 01885v1 Announce Type: new Abstract: While deep learning models have achieved state-of-the-art performance in time series anomaly detection, their complex architectures incur substantial inference overhead.
arXiv AI
Jul 2
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
arXiv:2602. 01359v3 Announce Type: replace-cross Abstract: Although recent studies on time-series anomaly detection have increasingly adopted ever-larger neural network architectures such as transformers and foundation models, they incur high computational costs and memory usage, making them impractical for real-time and resource-constrained scenarios.