EEG emotion signal feature extraction is computationally intensive and the classification accuracy of the model is not high. Therefore, it can seriously affect the overall performance of classification algorithms. In ...
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The ecological threat is produced by IT industries that are the critical frontrunners of the universal greenhouse gasses with a massive increase in hazardous carbon emission that accumulates gradually due to the compu...
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The hiring process for IT positions is often complex and time-consuming, posing significant challenges for businesses seeking to maintain consistency and efficiency. Interview scheduling is complicated by manual effor...
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In semiconductor manufacturing, wafer defect recognition plays a critical role. As the technology advances and wafer feature sizes decrease, defect detection has become more challenging, particularly for mix-type defe...
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In this paper, a collision-free target tracking control approach utilizing guided vector field guidance is proposed. In particular, a synthesized vector field is designed to direct the snake robot toward the target wh...
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A deep learning network is super-efficient for image processing. The artificial brain of the deep learning system is highly capable of solving the categorization of problems. However, deep learning has several restric...
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Local feature-based approaches are proven to be most successful for the application of human action recognition. This work aims to study the effect of the number of cuboids chosen on the performance of human action re...
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Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure learning algorithms often need to central...
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Switch getting to know has become increasingly famous in many aspects of pc vision, including picture segmentation. This paper explores switch-gaining knowledge for automated brain tumor segmentation using a deep gett...
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Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are traditionally believed to improve gener...
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Data augmentation (DA) is a powerful workhorse for bolstering performance in modern machine learning. Specific augmentations like translations and scaling in computer vision are traditionally believed to improve generalization by generating new (artificial) data from the same distribution. However, this traditional viewpoint does not explain the success of prevalent augmentations in modern machine learning (e.g. randomized masking, cutout, mixup), that greatly alter the training data distribution. In this work, we develop a new theoretical framework to characterize the impact of a general class of DA on underparameterized and overparameterized linear model generalization. Our framework reveals that DA induces implicit spectral regularization through a combination of two distinct effects: a) manipulating the relative proportion of eigenvalues of the data covariance matrix in a training-data-dependent manner, and b) uniformly boosting the entire spectrum of the data covariance matrix through ridge regression. These effects, when applied to popular augmentations, give rise to a wide variety of phenomena, including discrepancies in generalization between over-parameterized and under-parameterized regimes and differences between regression and classification tasks. Our framework highlights the nuanced and sometimes surprising impacts of DA on generalization, and serves as a testbed for novel augmentation design.
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