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Do not sleep on traditional machine learning: Simple and interpretable techniques are competitive to deep learning for sleep scoring.

, , , , , , and . Biomed. Signal Process. Control., (March 2023)

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Do Not Sleep on Linear Models: Simple and Interpretable Techniques Outperform Deep Learning for Sleep Scoring., , , , , , and . CoRR, (2022)Assessing the added value of context during stress detection from wearable data., , , , , , , and . BMC Medical Informatics Decis. Mak., 22 (1): 268 (2022)MIRRA: A Modular and Cost-Effective Microclimate Monitoring System for Real-Time Remote Applications., , , , , , , , and . Sensors, 21 (13): 4615 (2021)Plotly-Resampler: Effective Visual Analytics for Large Time Series., , , and . CoRR, (2022)tsdownsample: high-performance time series downsampling for scalable visualization., , and . CoRR, (2023)MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB., , , and . CoRR, (2023)Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals., , , , and . SEAMS@ICSE, page 20-30. ACM, (2020)Data Point Selection for Line Chart Visualization: Methodological Assessment and Evidence-Based Guidelines., , , and . CoRR, (2023)Towards Knowledge-Driven Symptom Monitoring & Trigger Detection of Primary Headache Disorders., , , , , , , , , and . WWW (Companion Volume), page 264-268. ACM, (2022)MinMaxLTTB: Leveraging MinMax-Preselection to Scale LTTB., , , and . IEEE VIS (Short Papers), page 21-25. IEEE, (2023)