Copertina

An Automated Approach to Collecting and Labeling Time Series Data for Event Detection Using Elastic Node Hardware

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Ling, Tianheng, Mansour, Islam, Qian, Chao, Schiele, Gregor
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

On-Device Soft Sensors: Real-Time Fluid Flow Estimation from Level Sensor Data
di: Ling, Tianheng, et al.
Pubblicazione: (2023)

On-device AI: Quantization-aware Training of Transformers in Time-Series
di: Ling, Tianheng, et al.
Pubblicazione: (2024)

ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing
di: Qian, Chao, et al.
Pubblicazione: (2024)

Integer-only Quantized Transformers for Embedded FPGA-based Time-series Forecasting in AIoT
di: Ling, Tianheng, et al.
Pubblicazione: (2024)

Automating Versatile Time-Series Analysis with Tiny Transformers on Embedded FPGAs
di: Ling, Tianheng, et al.
Pubblicazione: (2025)

Resource-aware Mixed-precision Quantization for Enhancing Deployability of Transformers for Time-series Forecasting on Embedded FPGAs
di: Ling, Tianheng, et al.
Pubblicazione: (2024)

Automated Energy-Aware Time-Series Model Deployment on Embedded FPGAs for Resilient Combined Sewer Overflow Management
di: Ling, Tianheng, et al.
Pubblicazione: (2025)

Enabling Vibration-Based Gesture Recognition on Everyday Furniture via Energy-Efficient FPGA Implementation of 1D Convolutional Networks
di: Shibata, Koki, et al.
Pubblicazione: (2025)

Towards Auto-Building of Embedded FPGA-based Soft Sensors for Wastewater Flow Estimation
di: Ling, Tianheng, et al.
Pubblicazione: (2024)

FlowPrecision: Advancing FPGA-Based Real-Time Fluid Flow Estimation with Linear Quantization
di: Ling, Tianheng, et al.
Pubblicazione: (2024)

Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity
di: Qian, Chao, et al.
Pubblicazione: (2024)

A Resilient Solution for Sewer Overflow Monitoring across Cloud and Edge
di: Singh, Vipin, et al.
Pubblicazione: (2026)

Evaluating Time Series Models for Urban Wastewater Management: Predictive Performance, Model Complexity and Resilience
di: Singh, Vipin, et al.
Pubblicazione: (2025)

StrikeWatch: Wrist-worn Gait Recognition with Compact Time-series Models on Low-power FPGAs
di: Ling, Tianheng, et al.
Pubblicazione: (2025)

Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
di: Yang, Yuxuan, et al.
Pubblicazione: (2025)

Using Subgraph GNNs for Node Classification:an Overlooked Potential Approach
di: Zeng, Qian, et al.
Pubblicazione: (2025)

Why Do Class-Dependent Evaluation Effects Occur with Time Series Feature Attributions? A Synthetic Data Investigation
di: Baer, Gregor, et al.
Pubblicazione: (2025)

Semiconductor Industry Trend Prediction with Event Intervention Based on LSTM Model in Sentiment-Enhanced Time Series Data
di: Yen, Wei-hsiang, et al.
Pubblicazione: (2025)

Improving Detection of Rare Nodes in Hierarchical Multi-Label Learning
di: Xu, Isaac, et al.
Pubblicazione: (2026)

Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
di: Baer, Gregor, et al.
Pubblicazione: (2025)

PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes
di: Zhou, Yiming, et al.
Pubblicazione: (2026)

Data-driven Modeling of Combined Sewer Systems for Urban Sustainability: An Empirical Evaluation
di: Singh, Vipin, et al.
Pubblicazione: (2024)

Posterior Label Smoothing for Node Classification
di: Heo, Jaeseung, et al.
Pubblicazione: (2024)

Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining
di: Yan, Xu, et al.
Pubblicazione: (2024)

Capture Timing-Attention of Events in Clinical Time Series
di: Li, Jia, et al.
Pubblicazione: (2026)

Measuring Pre-training Data Quality without Labels for Time Series Foundation Models
di: Wen, Songkang, et al.
Pubblicazione: (2024)

xaitimesynth: A Python Package for Evaluating Attribution Methods for Time Series with Synthetic Ground Truth
di: Baer, Gregor
Pubblicazione: (2026)

EventTSF: Event-Aware Non-Stationary Time Series Forecasting
di: Ge, Yunfeng, et al.
Pubblicazione: (2025)

Labels Matter More Than Models: Rethinking the Unsupervised Paradigm in Time Series Anomaly Detection
di: Zhong, Zhijie, et al.
Pubblicazione: (2025)

Hardware-Accelerated Event-Graph Neural Networks for Low-Latency Time-Series Classification on SoC FPGA
di: Nakano, Hiroshi, et al.
Pubblicazione: (2025)

Contrast Similarity-Aware Dual-Pathway Mamba for Multivariate Time Series Node Classification
di: Du, Mingsen, et al.
Pubblicazione: (2024)

Data Collection and Labeling Techniques for Machine Learning
di: Huang, Qianyu, et al.
Pubblicazione: (2024)

Let Experts Feel Uncertainty: A Multi-Expert Label Distribution Approach to Probabilistic Time Series Forecasting
di: Zhou, Zhen, et al.
Pubblicazione: (2026)

IRNN: Innovation-driven Recurrent Neural Network for Time-Series Data Modeling and Prediction
di: Zhou, Yifan, et al.
Pubblicazione: (2025)

Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation
di: Choi, Kukjin, et al.
Pubblicazione: (2024)

Structured Exploration and Exploitation of Label Functions for Automated Data Annotation
di: Lam, Phong, et al.
Pubblicazione: (2026)

Active Learning and Transfer Learning for Anomaly Detection in Time-Series Data
di: Kelleher, John D., et al.
Pubblicazione: (2025)

From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations
di: Hao, Xinli, et al.
Pubblicazione: (2024)

Gait Event Detection and Travel Distance Using Waist-Worn Accelerometers across a Range of Speeds: Automated Approach
di: Ramli, Albara Ah, et al.
Pubblicazione: (2023)

SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection
di: Zhong, Zhijie, et al.
Pubblicazione: (2024)