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07/14/2023
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data
Source-free domain adaptation (SFDA) aims to adapt a pretrained model fr...
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02/13/2023
Label-efficient Time Series Representation Learning: A Review
The scarcity of labeled data is one of the main challenges of applying d...
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12/03/2022
CoTMix: Contrastive Domain Adaptation for Time-Series via Temporal Mixup
Unsupervised Domain Adaptation (UDA) has emerged as a powerful solution ...
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09/14/2022
PAPyA: Performance Analysis of Large RDF Graphs Processing Made Easy
Prescriptive Performance Analysis (PPA) has shown to be more useful than...
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08/13/2022
Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
Learning time-series representations when only unlabeled data or few lab...
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06/16/2022
Domain Generalization via Selective Consistency Regularization for Time Series Classification
Domain generalization methods aim to learn models robust to domain shift...
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03/15/2022
ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data
Unsupervised domain adaptation methods aim to generalize well on unlabel...
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11/29/2021
Self-supervised Autoregressive Domain Adaptation for Time Series Data
Unsupervised domain adaptation (UDA) has successfully addressed the doma...
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07/09/2021
Adversarial Domain Adaptation with Self-Training for EEG-based Sleep Stage Classification
Sleep staging is of great importance in the diagnosis and treatment of s...
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06/26/2021
Time-Series Representation Learning via Temporal and Contextual Contrasting
Learning decent representations from unlabeled time-series data with tem...
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02/17/2021
Robust Domain-Free Domain Generalization with Class-aware Alignment
While deep neural networks demonstrate state-of-the-art performance on a...
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12/11/2020
The Future is Big Graphs! A Community View on Graph Processing Systems
Graphs are by nature unifying abstractions that can leverage interconnec...
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07/20/2020
