{"list":"other","total":52,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1807.03748","title":"Representation Learning with Contrastive Predictive Coding","created":"2018-07-10","cited_by_count":7777},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"2001.07685","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","created":"2020-01-21","cited_by_count":2085},{"id":"1803.01271","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","created":"2018-03-04","cited_by_count":1476},{"id":"1702.05464","title":"Adversarial Discriminative Domain Adaptation","created":"2017-02-17","cited_by_count":1311},{"id":"1411.1792","title":"How transferable are features in deep neural networks?","created":"2014-11-06","cited_by_count":1225},{"id":"1809.04356","title":"Deep learning for time series classification: a review","created":"2018-09-12","cited_by_count":576},{"id":"2106.10466","title":"TS2Vec: Towards Universal Representation of Time Series","created":"2021-06-19","cited_by_count":471},{"id":"2010.02803","title":"A Transformer-based Framework for Multivariate Time Series Representation Learning","created":"2020-10-06","cited_by_count":453},{"id":"1511.05547","title":"Return of Frustratingly Easy Domain Adaptation","created":"2015-11-17","cited_by_count":391},{"id":"1909.04939","title":"InceptionTime: Finding AlexNet for Time Series Classification","created":"2019-09-11","cited_by_count":387},{"id":"1611.06455","title":"Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline","created":"2016-11-20","cited_by_count":360},{"id":"2106.14112","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","created":"2021-06-26","cited_by_count":345},{"id":"2206.08496","title":"Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency","created":"2022-06-17","cited_by_count":250},{"id":"2002.12478","title":"Time Series Data Augmentation for Deep Learning: A Survey","created":"2020-02-27","cited_by_count":226},{"id":"2106.00750","title":"Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding","created":"2021-06-01","cited_by_count":224},{"id":"1703.04046","title":"DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG","created":"2017-03-12","cited_by_count":157},{"id":"2007.15951","title":"An Empirical Survey of Data Augmentation for Time Series Classification with Neural Networks","created":"2020-07-31","cited_by_count":128},{"id":"2007.16104","title":"Uncovering the structure of clinical EEG signals with self-supervised learning","created":"2020-07-31","cited_by_count":109},{"id":"1907.11879","title":"Multi-task Self-Supervised Learning for Human Activity Detection","created":"2019-07-27","cited_by_count":102},{"id":"2002.03898","title":"Self-supervised ECG Representation Learning for Emotion Recognition","created":"2020-02-04","cited_by_count":99},{"id":"2107.14293","title":"Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series","created":"2021-07-29","cited_by_count":96},{"id":"2202.04770","title":"Unsupervised Time-Series Representation Learning with Iterative Bilinear Temporal-Spectral Fusion","created":"2022-02-08","cited_by_count":96},{"id":"2005.10996","title":"Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data","created":"2020-05-22","cited_by_count":82},{"id":"2102.06828","title":"Domain Adaptation for Time Series Forecasting via Attention Sharing","created":"2021-02-13","cited_by_count":68},{"id":"1811.01533","title":"Transfer learning for time series classification","created":"2018-11-05","cited_by_count":60},{"id":"2302.03133","title":"Domain Adaptation for Time Series Under Feature and Label Shifts","created":"2023-02-06","cited_by_count":60},{"id":"2203.09270","title":"Mixing Up Contrastive Learning: Self-Supervised Representation Learning for Time Series","created":"2022-03-17","cited_by_count":54},{"id":"2012.11797","title":"Time Series Domain Adaptation via Sparse Associative Structure Alignment","created":"2020-12-22","cited_by_count":51},{"id":"2002.10061","title":"Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification","created":"2020-02-24","cited_by_count":48},{"id":"2203.08321","title":"ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data","created":"2022-03-15","cited_by_count":45},{"id":"2208.06616","title":"Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification","created":"2022-08-13","cited_by_count":39},{"id":"2111.14834","title":"Self-supervised Autoregressive Domain Adaptation for Time Series Data","created":"2021-11-29","cited_by_count":29},{"id":"2212.01141","title":"MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series","created":"2022-12-02","cited_by_count":28},{"id":"2109.07839","title":"Self-supervised Contrastive Learning for EEG-based Sleep Staging","created":"2021-09-16","cited_by_count":27},{"id":"2206.02353","title":"Beyond Just Vision: A Review on Self-Supervised Representation Learning on Multimodal and Temporal Data","created":"2022-06-06","cited_by_count":27},{"id":"1811.03760","title":"EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction","created":"2018-11-09","cited_by_count":26},{"id":"2204.03342","title":"Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift","created":"2022-04-07","cited_by_count":24},{"id":"2009.13233","title":"Sense and Learn: Self-Supervision for Omnipresent Sensors","created":"2020-09-28","cited_by_count":21},{"id":"2212.01555","title":"Contrastive Domain Adaptation for Time-Series via Temporal Mixup","created":"2022-12-03","cited_by_count":18},{"id":"1910.09505","title":"Multi-Resolution Weak Supervision for Sequential Data","created":"2019-10-21","cited_by_count":17},{"id":"2107.04470","title":"ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training","created":"2021-07-09","cited_by_count":17},{"id":"2206.11517","title":"Utilizing Expert Features for Contrastive Learning of Time-Series Representations","created":"2022-06-23","cited_by_count":17},{"id":"2012.04479","title":"Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks","created":"2020-12-05","cited_by_count":14},{"id":"2210.06286","title":"Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluation","created":"2022-10-10","cited_by_count":14},{"id":"2109.07455","title":"Deep Bregman Divergence for Contrastive Learning of Visual Representations","created":"2021-09-15","cited_by_count":13},{"id":"2202.05400","title":"PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition","created":"2022-02-11","cited_by_count":12},{"id":"2110.00578","title":"SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time Series","created":"2021-10-01","cited_by_count":9},{"id":"2110.13623","title":"Contrastive Neural Processes for Self-Supervised Learning","created":"2021-10-24","cited_by_count":9},{"id":"2207.07897","title":"Transfer learning for time series classification using synthetic data generation","created":"2022-07-16","cited_by_count":5}],"next_cursor":null}