Results 11 to 20 of about 117,002 (264)
Sample design, sample augmentation, and estimation for Wave 2 of the NSHAP. [PDF]
J Gerontol B Psychol Sci Soc Sci, 2014 The sample for the second wave (2010) of National Social Life, Health, and Aging Project (NSHAP) was designed to increase the scientific value of the Wave 1 (2005) data set by revisiting sample members 5 years after their initial interviews and augmenting this sample where possible.There were 2 important innovations.
O'Muircheartaigh C +3 more
europepmc +5 more sources
ParticleAugment: Sampling-based data augmentation
Computer Vision and Image Understanding, 2023 8 ...
Alexander Tsaregorodtsev +1 more
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Mixed Sample Augmentation for Online Distillation
ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 Mixed Sample Regularization (MSR), such as MixUp or CutMix, is a powerful data augmentation strategy to generalize convolutional neural networks. Previous empirical analysis has illustrated an orthogonal performance gain between MSR and conventional offline Knowledge Distillation (KD).
Yiqing Shen 0003 +4 more
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Augmented Negative Sampling for Collaborative Filtering
Proceedings of the 17th ACM Conference on Recommender Systems, 2023 Negative sampling is essential for implicit-feedback-based collaborative filtering, which is used to constitute negative signals from massive unlabeled data to guide supervised learning. The state-of-the-art idea is to utilize hard negative samples that carry more useful information to form a better decision boundary.
Yuhan Zhao 0001 +5 more
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Reject inference, augmentation, and sample selection [PDF]
European Journal of Operational Research, 2007 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Banasik, John, Crook, Jonathan
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Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation
Algorithms, 2022 Data augmentation methods enrich datasets with augmented data to improve the performance of neural networks. Recently, automated data augmentation methods have emerged, which automatically design augmentation strategies. The existing work focuses on image classification and object detection, whereas we provide the first study on semantic image ...
Misgana Negassi +2 more
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CoRR, 2022 Data-Augmentation (DA) is known to improve performance across tasks and datasets. We propose a method to theoretically analyze the effect of DA and study questions such as: how many augmented samples are needed to correctly estimate the information encoded by that DA? How does the augmentation policy impact the final parameters of a model?
Randall Balestriero +2 more
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Data Augmentation with Variational Autoencoders and Manifold Sampling [PDF]
, 2021 We propose a new efficient way to sample from a Variational Autoencoder in the challenging low sample size setting. This method reveals particularly well suited to perform data augmentation in such a low data regime and is validated across various standard and real-life data sets.
Chadebec, Clément +1 more
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Sample Efficiency of Data Augmentation Consistency Regularization
CoRR, 2022 Data augmentation is popular in the training of large neural networks; currently, however, there is no clear theoretical comparison between different algorithmic choices on how to use augmented data. In this paper, we take a step in this direction - we first present a simple and novel analysis for linear regression with label invariant augmentations ...
Shuo Yang +5 more
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Sequence-Level Mixed Sample Data Augmentation [PDF]
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 EMNLP ...
Demi Guo, Yoon Kim, Alexander M. Rush
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