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  "author": "Nauman Ahad",
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  "author": "Nauman Ahad",
            
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  "defined_in": "https://doi.org/10.48550/arXiv.2009.11829",
            
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  "doi": "10.57702/1mx8clid",
            
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  "doi_date_published": "2024-12-16",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Mark A. Davenport",
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      "extra_author": "Mark A. Davenport",
            
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      "name": "change-point-detection",
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      "name": "change-point-detection",
            
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      "title": "Change Point Detection"
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      "title": "Change Point Detection"
            
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      "name": "semi-supervised-learning",
            
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      "title": "Semi-supervised Learning"
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      "title": "Semi-supervised Learning"
            
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  "name": 
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  "name": 
            
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emi-supervised-sequence-classi-cation-through-change-point-detection",
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emi-supervised-sequence-classi-cation-through-change-point-detection",
            
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  "notes": "Sequential sensor data is generated in a wide variety of 
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  "notes": "Sequential sensor data is generated in a wide variety of 
            
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practical applications. A fundamental challenge involves learning 
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practical applications. A fundamental challenge involves learning 
            
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e\ufb00ective classi\ufb01ers for such sequential data. While deep 
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e\ufb00ective classi\ufb01ers for such sequential data. While deep 
            
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learning has led to impressive performance gains in recent years in 
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learning has led to impressive performance gains in recent years in 
            
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domains such as speech, this has relied on the availability of large 
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domains such as speech, this has relied on the availability of large 
            
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datasets of sequences with high-quality labels. In many applications, 
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datasets of sequences with high-quality labels. In many applications, 
            
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however, the associated class labels are often extremely limited, with 
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however, the associated class labels are often extremely limited, with 
            
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precise labelling/segmentation being too expensive to perform at a 
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precise labelling/segmentation being too expensive to perform at a 
            
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high volume. However, large amounts of unlabeled data may still be 
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high volume. However, large amounts of unlabeled data may still be 
            
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available. In this paper we propose a novel framework for 
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available. In this paper we propose a novel framework for 
            
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semi-supervised learning in such contexts.",
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semi-supervised learning in such contexts.",
            
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  "title": "Semi-supervised sequence classi\ufb01cation through change 
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  "title": "Semi-supervised sequence classi\ufb01cation through change 
            
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point detection",
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point detection",
            
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}