{"total":82,"rows":[{"id":"2110.02642","title":"Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy","created":"2021-10-06","cited_by_count":394,"area":null},{"id":"2306.10347","title":"DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection","created":"2023-06-17","cited_by_count":103,"area":null},{"id":"2105.11043","title":"SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification","created":"2021-05-23","cited_by_count":84,"area":null},{"id":"2206.00888","title":"Squeezeformer: An Efficient Transformer for Automatic Speech Recognition","created":"2022-06-02","cited_by_count":51,"area":null},{"id":"2111.08446","title":"Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directions","created":"2021-11-03","cited_by_count":43,"area":null},{"id":"2105.13864","title":"SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Staging","created":"2021-05-24","cited_by_count":32,"area":null},{"id":"2109.07839","title":"Self-supervised Contrastive Learning for EEG-based Sleep Staging","created":"2021-09-16","cited_by_count":27,"area":null},{"id":"2101.02452","title":"RobustSleepNet: Transfer learning for automated sleep staging at scale","created":"2021-01-07","cited_by_count":24,"area":null},{"id":"2208.05836","title":"HyperTime: Implicit Neural Representation for Time Series","created":"2022-08-11","cited_by_count":24,"area":null},{"id":"2106.13695","title":"CADDA: Class-wise Automatic Differentiable Data Augmentation for EEG Signals","created":"2021-06-25","cited_by_count":22,"area":null},{"id":"2111.04418","title":"A Survey of Human Activity Recognition in Smart Homes Based on IoT Sensors Algorithms: Taxonomies, Challenges, and Opportunities with Deep Learning","created":"2021-10-18","cited_by_count":21,"area":null},{"id":"2401.05363","title":"Generalizable Sleep Staging via Multi-Level Domain Alignment","created":"2023-12-13","cited_by_count":17,"area":null},{"id":"2408.07773","title":"MedTsLLM: Leveraging LLMs for Multimodal Medical Time Series Analysis","created":"2024-08-14","cited_by_count":15,"area":null},{"id":"2410.16424","title":"Promoting cross-modal representations to improve multimodal foundation models for physiological signals","created":"2024-10-21","cited_by_count":13,"area":null},{"id":"2209.11173","title":"U-Sleep's resilience to AASM guidelines","created":"2022-09-19","cited_by_count":12,"area":null},{"id":"2401.09793","title":"PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection","created":"2024-01-18","cited_by_count":12,"area":null},{"id":"2110.08255","title":"Yformer: U-Net Inspired Transformer Architecture for Far Horizon Time Series Forecasting","created":"2021-10-13","cited_by_count":11,"area":null},{"id":"2111.10026","title":"IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal","created":"2021-11-19","cited_by_count":10,"area":null},{"id":"2112.03379","title":"Deep Efficient Continuous Manifold Learning for Time Series Modeling","created":"2021-12-03","cited_by_count":10,"area":null},{"id":"2503.10198","title":"Deep Learning for Time Series Forecasting: A Survey","created":"2025-03-13","cited_by_count":10,"area":null},{"id":"2203.12590","title":"TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Staging","created":"2022-03-22","cited_by_count":8,"area":null},{"id":"2206.08672","title":"A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking Applications","created":"2022-06-17","cited_by_count":7,"area":null},{"id":"2209.09452","title":"SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning","created":"2022-09-20","cited_by_count":7,"area":null},{"id":"2412.12159","title":"Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptation","created":"2024-12-11","cited_by_count":7,"area":null},{"id":"2302.10182","title":"PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operations","created":"2023-01-27","cited_by_count":5,"area":null},{"id":"2411.11641","title":"TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection","created":"2024-11-18","cited_by_count":5,"area":null},{"id":"2203.07861","title":"Don't Get Me Wrong: How to Apply Deep Visual Interpretations to Time Series","created":"2022-03-14","cited_by_count":4,"area":null},{"id":"2205.03578","title":"Automatic Detection of Interplanetary Coronal Mass Ejections in Solar Wind In Situ Data","created":"2022-05-07","cited_by_count":4,"area":null},{"id":"2401.01479","title":"Kernel-U-Net: Multivariate Time Series Forecasting using Custom Kernels","created":"2024-01-03","cited_by_count":4,"area":null},{"id":"2408.11884","title":"ST-USleepNet: A Spatial-Temporal Coupling Prominence Network for Multi-Channel Sleep Staging","created":"2024-08-21","cited_by_count":4,"area":null},{"id":"2312.11939","title":"Time-Series Contrastive Learning against False Negatives and Class Imbalance","created":"2023-12-19","cited_by_count":3,"area":null},{"id":"2407.04753","title":"Continuous Sleep Depth Index Annotation with Deep Learning Yields Novel Digital Biomarkers for Sleep Health","created":"2024-07-05","cited_by_count":3,"area":null},{"id":"2408.04377","title":"Anomaly Prediction: A Novel Approach with Explicit Delay and Horizon","created":"2024-08-08","cited_by_count":3,"area":null},{"id":"2408.15253","title":"A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Input","created":"2024-08-09","cited_by_count":3,"area":null},{"id":"2510.20084","title":"ShapeX: Shapelet-Driven Post Hoc Explanations for Time Series Classification Models","created":"2025-10-23","cited_by_count":3,"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":"2404.15342","title":"WaveSleepNet: An Interpretable Network for Expert-like Sleep Staging","created":"2024-04-11","cited_by_count":2,"area":null},{"id":"2503.04582","title":"PSDNorm: Test-Time Temporal Normalization for Deep Learning in Sleep Staging","created":"2025-03-06","cited_by_count":2,"area":null},{"id":"2504.00336","title":"Large EEG-U-Transformer for Time-Step Level Detection Without Pre-Training","created":"2025-04-01","cited_by_count":2,"area":null},{"id":"2107.10746","title":"High Frequency EEG Artifact Detection with Uncertainty via Early Exit Paradigm","created":"2021-07-21","cited_by_count":1,"area":null},{"id":"2209.11174","title":"SERF: Interpretable Sleep Staging using Embeddings, Rules, and Features","created":"2022-09-21","cited_by_count":1,"area":null},{"id":"2211.13125","title":"EEG aided boosting of single-lead ECG based sleep staging with Deep Knowledge Distillation","created":"2022-11-18","cited_by_count":1,"area":null},{"id":"2212.05669","title":"Development of Personalized Sleep Induction System based on Mental States","created":"2022-12-12","cited_by_count":1,"area":null},{"id":"2212.07771","title":"Temporal Saliency Detection Towards Explainable Transformer-based Timeseries Forecasting","created":"2022-12-15","cited_by_count":1,"area":null},{"id":"2304.06033","title":"Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Models","created":"2023-03-28","cited_by_count":1,"area":null},{"id":"2305.09543","title":"EEG-based Sleep Staging with Hybrid Attention","created":"2023-05-16","cited_by_count":1,"area":null},{"id":"2310.02032","title":"aSAGA: Automatic Sleep Analysis with Gray Areas","created":"2023-10-03","cited_by_count":1,"area":null},{"id":"2312.03196","title":"Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging","created":"2023-12-06","cited_by_count":1,"area":null},{"id":"2509.22810","title":"Introducing Multimodal Paradigm for Learning Sleep Staging PSG via General-Purpose Model","created":"2025-09-26","cited_by_count":1,"area":null},{"id":"2510.07524","title":"EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learning","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2606.08583","title":"A spectral audit framework reveals task-dependent aperiodic reliance across EEG and ECG deep learning","created":"2026-06-07","cited_by_count":1,"area":null},{"id":"2101.02538","title":"MRNet: a Multi-scale Residual Network for EEG-based Sleep Staging","created":"2021-01-07","cited_by_count":0,"area":null},{"id":"2101.04948","title":"Deep State Inference: Toward Behavioral Model Inference of Black-box Software Systems","created":"2021-01-13","cited_by_count":0,"area":null},{"id":"2102.08245","title":"Classification of multivariate weakly-labelled time-series with attention","created":"2021-02-16","cited_by_count":0,"area":null},{"id":"2107.08442","title":"Sleep Staging Based on Multi Scale Dual Attention Network","created":"2021-07-18","cited_by_count":0,"area":null},{"id":"2201.04188","title":"Dynamic Price of Parking Service based on Deep Learning","created":"2022-01-11","cited_by_count":0,"area":null},{"id":"2205.14976","title":"Rethinking Saliency Map: An Context-aware Perturbation Method to Explain EEG-based Deep Learning Model","created":"2022-05-30","cited_by_count":0,"area":null},{"id":"2211.03282","title":"Performance and utility trade-off in interpretable sleep staging","created":"2022-11-07","cited_by_count":0,"area":null},{"id":"2212.13919","title":"Siamese Sleep Transformer For Robust Sleep Stage Scoring With Self-knowledge Distillation and Selective Batch Sampling","created":"2022-12-12","cited_by_count":0,"area":null},{"id":"2302.12709","title":"Sleep Model -- A Sequence Model for Predicting the Next Sleep Stage","created":"2023-02-17","cited_by_count":0,"area":null},{"id":"2305.03178","title":"Contrastive Learning for Sleep Staging based on Inter Subject Correlation","created":"2023-05-05","cited_by_count":0,"area":null},{"id":"2308.02416","title":"Local-Global Temporal Fusion Network with an Attention Mechanism for Multiple and Multiclass Arrhythmia Classification","created":"2023-08-03","cited_by_count":0,"area":null},{"id":"2404.17276","title":"Efficient Deterministic Renewable Energy Forecasting Guided by Multiple-Location Weather Data","created":"2024-04-26","cited_by_count":0,"area":null},{"id":"2408.12792","title":"Boundary Density Likelihood for Direct Event-Time Supervision","created":"2024-08-23","cited_by_count":0,"area":null},{"id":"2410.03385","title":"From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals","created":"2024-10-04","cited_by_count":0,"area":null},{"id":"2410.19819","title":"Automatic Classification of Sleep Stages from EEG Signals Using Riemannian Metrics and Transformer Networks","created":"2024-10-18","cited_by_count":0,"area":null},{"id":"2412.10814","title":"Learning Satellite Pattern-of-Life Identification: A Diffusion-based Approach","created":"2024-12-14","cited_by_count":0,"area":null},{"id":"2501.18196","title":"GDformer: Going Beyond Subsequence Isolation for Multivariate Time Series Anomaly Detection","created":"2025-01-30","cited_by_count":0,"area":null},{"id":"2502.17470","title":"MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification","created":"2025-02-13","cited_by_count":0,"area":null},{"id":"2503.22809","title":"Data-Driven Worker Activity Recognition and Efficiency Estimation in Manual Fruit Harvesting","created":"2025-03-28","cited_by_count":0,"area":null},{"id":"2504.17617","title":"Decentralized Time Series Classification with ROCKET Features","created":"2025-04-24","cited_by_count":0,"area":null},{"id":"2505.17640","title":"A Network Science Approach to Granular Time Series Segmentation","created":"2025-05-23","cited_by_count":0,"area":null},{"id":"2508.15215","title":"Multi-Channel Differential Transformer for Cross-Domain Sleep Stage Classification with Heterogeneous EEG and EOG","created":"2025-08-21","cited_by_count":0,"area":null},{"id":"2509.02982","title":"StableSleep: Source-Free Test-Time Adaptation for Sleep Staging with Lightweight Safety Rails","created":"2025-09-03","cited_by_count":0,"area":null},{"id":"2510.09930","title":"Perseus: Interactive Time Series Segmentation with Sparse Supervision via Stateful Memory","created":"2025-10-11","cited_by_count":0,"area":null},{"id":"2511.07651","title":"Enhancing Binary Encoded Crime Linkage Analysis Using Siamese Network","created":"2025-11-10","cited_by_count":0,"area":null},{"id":"2512.22976","title":"A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Staging","created":"2025-12-28","cited_by_count":0,"area":null},{"id":"2601.00459","title":"Combining Residual U-Net and Data Augmentation for Dense Temporal Segmentation of Spike Wave Discharges in Single-Channel EEG","created":"2026-01-01","cited_by_count":0,"area":null},{"id":"2601.13926","title":"SCG With Your Phone: Diagnosis of Rhythmic Spectrum Disorders in Field Conditions","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2605.03085","title":"Adaptive Data Compression and Reconstruction for Memory-Bounded EEG Continual Learning","created":"2026-05-04","cited_by_count":0,"area":null},{"id":"2608.18576","title":"Beyond receptive fields: sequence-pooled normalization can supply most of a sequence labeler's context","created":"2026-08-19","cited_by_count":0,"area":null},{"id":"2610.03911","title":"COVER: Learning to Accept More in Selective Sleep Staging","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}