{"total":122,"rows":[{"id":"2306.10125","title":"Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects","created":"2023-06-16","cited_by_count":99,"area":null},{"id":"2206.02909","title":"Self-supervised Learning for Human Activity Recognition Using 700,000 Person-days of Wearable Data","created":"2022-06-06","cited_by_count":75,"area":null},{"id":"2302.02515","title":"Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey","created":"2023-02-06","cited_by_count":68,"area":null},{"id":"2202.12938","title":"Assessing the State of Self-Supervised Human Activity Recognition using Wearables","created":"2022-02-22","cited_by_count":65,"area":null},{"id":"2111.00418","title":"Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances","created":"2021-10-31","cited_by_count":54,"area":null},{"id":"2206.13508","title":"Data Augmentation techniques in time series domain: A survey and taxonomy","created":"2022-06-25","cited_by_count":41,"area":null},{"id":"2202.05998","title":"What Makes Good Contrastive Learning on Small-Scale Wearable-based Tasks?","created":"2022-02-12","cited_by_count":40,"area":null},{"id":"2405.09591","title":"A Comprehensive Survey on Data Augmentation","created":"2024-05-15","cited_by_count":40,"area":null},{"id":"2310.20071","title":"FOCAL: Contrastive Learning for Multimodal Time-Series Sensing Signals in Factorized Orthogonal Latent Space","created":"2023-10-30","cited_by_count":33,"area":null},{"id":"2106.05142","title":"Neighborhood Contrastive Learning Applied to Online Patient Monitoring","created":"2021-06-09","cited_by_count":32,"area":null},{"id":"2102.06073","title":"SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data","created":"2021-02-11","cited_by_count":31,"area":null},{"id":"2410.19818","title":"UniMTS: Unified Pre-training for Motion Time Series","created":"2024-10-18","cited_by_count":26,"area":null},{"id":"2305.11567","title":"TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series","created":"2023-05-19","cited_by_count":18,"area":null},{"id":"2303.17611","title":"Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition","created":"2023-03-29","cited_by_count":17,"area":null},{"id":"2102.03814","title":"MIN2Net: End-to-End Multi-Task Learning for Subject-Independent Motor Imagery EEG Classification","created":"2021-02-07","cited_by_count":16,"area":null},{"id":"2310.07756","title":"Self-supervised Representation Learning From Random Data Projectors","created":"2023-10-11","cited_by_count":15,"area":null},{"id":"2411.14452","title":"Past, Present, and Future of Sensor-Based Human Activity Recognition Using Wearables: A Surveying Tutorial on a Still Challenging Task","created":"2024-11-11","cited_by_count":15,"area":null},{"id":"2202.07036","title":"Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens","created":"2022-02-14","cited_by_count":13,"area":null},{"id":"2302.03357","title":"Towards Enhancing Time Series Contrastive Learning: A Dynamic Bad Pair Mining Approach","created":"2023-02-07","cited_by_count":13,"area":null},{"id":"2204.04360","title":"Data Augmentation for Electrocardiograms","created":"2022-04-09","cited_by_count":12,"area":null},{"id":"2306.04641","title":"Generalizable Low-Resource Activity Recognition with Diverse and Discriminative Representation Learning","created":"2023-05-25","cited_by_count":12,"area":null},{"id":"2312.04142","title":"TimeDRL: Disentangled Representation Learning for Multivariate Time-Series","created":"2023-12-07","cited_by_count":10,"area":null},{"id":"2305.18888","title":"A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation Learning","created":"2023-05-30","cited_by_count":9,"area":null},{"id":"2310.10060","title":"Data Augmentation for Time-Series Classification: An Extensive Empirical Study and Comprehensive Survey","created":"2023-10-16","cited_by_count":9,"area":null},{"id":"2310.14390","title":"Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition","created":"2023-10-22","cited_by_count":9,"area":null},{"id":"2312.02185","title":"Virtual Fusion with Contrastive Learning for Single Sensor-based Activity Recognition","created":"2023-12-01","cited_by_count":9,"area":null},{"id":"2102.08310","title":"Adaptive Weighting Scheme for Automatic Time-Series Data Augmentation","created":"2021-02-16","cited_by_count":8,"area":null},{"id":"2202.12935","title":"Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild","created":"2022-02-22","cited_by_count":8,"area":null},{"id":"2109.02054","title":"Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition","created":"2021-09-05","cited_by_count":7,"area":null},{"id":"2203.05692","title":"Lifelong Adaptive Machine Learning for Sensor-based Human Activity Recognition Using Prototypical Networks","created":"2022-03-11","cited_by_count":7,"area":null},{"id":"2303.14254","title":"Towards Diverse and Coherent Augmentation for Time-Series Forecasting","created":"2023-03-24","cited_by_count":7,"area":null},{"id":"2211.06173","title":"Investigating Enhancements to Contrastive Predictive Coding for Human Activity Recognition","created":"2022-11-11","cited_by_count":6,"area":null},{"id":"2406.04609","title":"Diverse Intra- and Inter-Domain Activity Style Fusion for Cross-Person Generalization in Activity Recognition","created":"2024-06-07","cited_by_count":6,"area":null},{"id":"2410.00003","title":"Large Language Model-Guided Semantic Alignment for Human Activity Recognition","created":"2024-09-12","cited_by_count":6,"area":null},{"id":"2210.06873","title":"Data augmentation on-the-fly and active learning in data stream classification","created":"2022-10-13","cited_by_count":5,"area":null},{"id":"2306.06579","title":"Improving Time Series Encoding with Noise-Aware Self-Supervised Learning and an Efficient Encoder","created":"2023-06-11","cited_by_count":5,"area":null},{"id":"2312.05790","title":"SimPSI: A Simple Strategy to Preserve Spectral Information in Time Series Data Augmentation","created":"2023-12-10","cited_by_count":5,"area":null},{"id":"2402.05960","title":"Phase-driven Domain Generalizable Learning for Nonstationary Time Series","created":"2024-02-05","cited_by_count":5,"area":null},{"id":"2405.00319","title":"Data Augmentation Policy Search for Long-Term Forecasting","created":"2024-05-01","cited_by_count":5,"area":null},{"id":"2102.05956","title":"The Benefit of the Doubt: Uncertainty Aware Sensing for Edge Computing Platforms","created":"2021-02-11","cited_by_count":4,"area":null},{"id":"2103.01119","title":"DTW-Merge: A Novel Data Augmentation Technique for Time Series Classification","created":"2021-03-01","cited_by_count":4,"area":null},{"id":"2205.15875","title":"SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time Series","created":"2022-05-31","cited_by_count":4,"area":null},{"id":"2408.10951","title":"Wave-Mask/Mix: Exploring Wavelet-Based Augmentations for Time Series Forecasting","created":"2024-08-20","cited_by_count":4,"area":null},{"id":"2409.13643","title":"Benchmarking Reliability of Deep Learning Models for Pathological Gait Classification","created":"2024-09-20","cited_by_count":4,"area":null},{"id":"2502.05783","title":"WatchGuardian: Enabling User-Defined Personalized Just-in-Time Intervention on Smartwatch","created":"2025-02-09","cited_by_count":4,"area":null},{"id":"2506.12322","title":"Machine Learning Methods for Small Data and Upstream Bioprocessing Applications: A Comprehensive Review","created":"2025-06-14","cited_by_count":4,"area":null},{"id":"2111.04185","title":"CoughTrigger: Earbuds IMU Based Cough Detection Activator Using An Energy-efficient Sensitivity-prioritized Time Series Classifier","created":"2021-11-07","cited_by_count":3,"area":null},{"id":"2210.06701","title":"Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep Learning","created":"2022-10-13","cited_by_count":3,"area":null},{"id":"2305.13541","title":"ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition","created":"2023-05-22","cited_by_count":3,"area":null},{"id":"2401.02255","title":"Balancing Continual Learning and Fine-tuning for Human Activity Recognition","created":"2024-01-04","cited_by_count":3,"area":null},{"id":"2403.04036","title":"Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift","created":"2024-03-06","cited_by_count":3,"area":null},{"id":"2406.06518","title":"Data Augmentation for Multivariate Time Series Classification: An Experimental Study","created":"2024-06-10","cited_by_count":3,"area":null},{"id":"2412.10912","title":"ST-FiT: Inductive Spatial-Temporal Forecasting with Limited Training Data","created":"2024-12-14","cited_by_count":3,"area":null},{"id":"2502.15901","title":"TS-OOD: Evaluating Time-Series Out-of-Distribution Detection and Prospective Directions for Progress","created":"2025-02-21","cited_by_count":3,"area":null},{"id":"2506.06917","title":"Graph-Based Physics-Guided Urban PM2.5 Air Quality Imputation with Constrained Monitoring Data","created":"2025-06-07","cited_by_count":3,"area":null},{"id":"2601.19040","title":"OATS: Online Data Augmentation for Time Series Foundation Models","created":"2026-01-26","cited_by_count":3,"area":null},{"id":"2101.02882","title":"Octave Mix: Data augmentation using frequency decomposition for activity recognition","created":"2021-01-08","cited_by_count":2,"area":null},{"id":"2102.03622","title":"Deep Semi-Supervised Learning for Time Series Classification","created":"2021-02-06","cited_by_count":2,"area":null},{"id":"2106.04114","title":"Theoretically Motivated Data Augmentation and Regularization for Portfolio Construction","created":"2021-06-08","cited_by_count":2,"area":null},{"id":"2209.00945","title":"IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications","created":"2022-09-02","cited_by_count":2,"area":null},{"id":"2210.07340","title":"LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learning","created":"2022-10-13","cited_by_count":2,"area":null},{"id":"2211.04509","title":"Care for the Mind Amid Chronic Diseases: An Interpretable AI Approach Using IoT","created":"2022-11-08","cited_by_count":2,"area":null},{"id":"2212.02622","title":"Towards a Taxonomy for the Use of Synthetic Data in Advanced Analytics","created":"2022-12-05","cited_by_count":2,"area":null},{"id":"2302.07998","title":"cGAN-Based High Dimensional IMU Sensor Data Generation for Enhanced Human Activity Recognition in Therapeutic Activities","created":"2023-02-16","cited_by_count":2,"area":null},{"id":"2304.06489","title":"Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey","created":"2023-04-07","cited_by_count":2,"area":null},{"id":"2308.12495","title":"Source-Free Collaborative Domain Adaptation via Multi-Perspective Feature Enrichment for Functional MRI Analysis","created":"2023-08-24","cited_by_count":2,"area":null},{"id":"2402.02275","title":"SudokuSens: Enhancing Deep Learning Robustness for IoT Sensing Applications using a Generative Approach","created":"2024-02-03","cited_by_count":2,"area":null},{"id":"2405.16456","title":"Dominant Shuffle: A Simple Yet Powerful Data Augmentation for Time-series Prediction","created":"2024-05-26","cited_by_count":2,"area":null},{"id":"2410.12606","title":"Self-Supervised Learning of Disentangled Representations for Multivariate Time-Series","created":"2024-10-16","cited_by_count":2,"area":null},{"id":"2502.17520","title":"On Neural Inertial Classification Networks for Pedestrian Activity Recognition","created":"2025-02-23","cited_by_count":2,"area":null},{"id":"2503.13542","title":"HAR-DoReMi: Optimizing Data Mixture for Self-Supervised Human Activity Recognition Across Heterogeneous IMU Datasets","created":"2025-03-16","cited_by_count":2,"area":null},{"id":"2507.04814","title":"UDF-GMA: Uncertainty Disentanglement and Fusion for General Movement Assessment","created":"2025-07-07","cited_by_count":2,"area":null},{"id":"2101.00702","title":"A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network","created":"2021-01-03","cited_by_count":1,"area":null},{"id":"2107.01086","title":"Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors","created":"2021-07-02","cited_by_count":1,"area":null},{"id":"2204.03737","title":"Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition","created":"2022-04-05","cited_by_count":1,"area":null},{"id":"2212.06536","title":"On Mini-Batch Training with Varying Length Time Series","created":"2022-12-13","cited_by_count":1,"area":null},{"id":"2304.04271","title":"Embarrassingly Simple MixUp for Time-series","created":"2023-04-09","cited_by_count":1,"area":null},{"id":"2406.05249","title":"A Language Model-Guided Framework for Mining Time Series with Distributional Shifts","created":"2024-06-07","cited_by_count":1,"area":null},{"id":"2410.11065","title":"Time Series Viewmakers for Robust Disruption Prediction","created":"2024-10-14","cited_by_count":1,"area":null},{"id":"2501.01472","title":"Augmented Contrastive Clustering with Uncertainty-Aware Prototyping for Time Series Test Time Adaptation","created":"2025-01-01","cited_by_count":1,"area":null},{"id":"2503.17978","title":"PIM: Physics-Informed Multi-task Pre-training for Improving Inertial Sensor-Based Human Activity Recognition","created":"2025-03-23","cited_by_count":1,"area":null},{"id":"2504.04613","title":"SiameseDuo++: Active Learning from Data Streams with Dual Augmented Siamese Networks","created":"2025-04-06","cited_by_count":1,"area":null},{"id":"2508.13284","title":"Physically Plausible Data Augmentations for Wearable IMU-based Human Activity Recognition Using Physics Simulation","created":"2025-08-18","cited_by_count":1,"area":null},{"id":"2511.11737","title":"DK-Root: A Joint Data-and-Knowledge-Driven Framework for Root Cause Analysis of QoE Degradations in Mobile Networks","created":"2025-11-13","cited_by_count":1,"area":null},{"id":"2603.00629","title":"Adapt Data to Model: Adaptive Transformation Optimization for Domain-shared Time Series Foundation Models","created":"2026-02-28","cited_by_count":1,"area":null},{"id":"2101.01073","title":"Anomaly Recognition from surveillance videos using 3D Convolutional Neural Networks","created":"2021-01-04","cited_by_count":0,"area":null},{"id":"2104.12284","title":"Accuracy Improvement for Fully Convolutional Networks via Selective Augmentation with Applications to Electrocardiogram Data","created":"2021-04-25","cited_by_count":0,"area":null},{"id":"2109.13192","title":"Consistency Training of Multi-exit Architectures for Sensor Data","created":"2021-09-27","cited_by_count":0,"area":null},{"id":"2111.03253","title":"Dynamic Data Augmentation with Gating Networks for Time Series Recognition","created":"2021-11-05","cited_by_count":0,"area":null},{"id":"2201.06147","title":"Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks","created":"2022-01-12","cited_by_count":0,"area":null},{"id":"2205.02977","title":"IMU Based Deep Stride Length Estimation With Self-Supervised Learning","created":"2022-05-06","cited_by_count":0,"area":null},{"id":"2205.14619","title":"Graph Structure Based Data Augmentation Method","created":"2022-05-29","cited_by_count":0,"area":null},{"id":"2208.00323","title":"A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance","created":"2022-07-30","cited_by_count":0,"area":null},{"id":"2208.08879","title":"SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical Processes","created":"2022-08-17","cited_by_count":0,"area":null},{"id":"2304.11265","title":"Estimating Motor Symptom Presence and Severity in Parkinson's Disease from Wrist Accelerometer Time Series using ROCKET and InceptionTime","created":"2023-04-21","cited_by_count":0,"area":null},{"id":"2307.05437","title":"Improving the Security of Smartwatch Payment with Deep Learning","created":"2023-07-11","cited_by_count":0,"area":null},{"id":"2311.05371","title":"Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data","created":"2023-11-09","cited_by_count":0,"area":null},{"id":"2404.15323","title":"Transportation mode recognition based on low-rate acceleration and location signals with an attention-based multiple-instance learning network","created":"2024-04-05","cited_by_count":0,"area":null},{"id":"2405.05015","title":"Concrete Dense Network for Long-Sequence Time Series Clustering","created":"2024-05-08","cited_by_count":0,"area":null},{"id":"2405.20590","title":"Class-Based Time Series Data Augmentation to Mitigate Extreme Class Imbalance for Solar Flare Prediction","created":"2024-05-31","cited_by_count":0,"area":null}],"next_cursor":"100"}