Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Dablain, Damien"'
Autor:
Dablain, Damien A., Chawla, Nitesh V.
Machine learning (ML) models have difficulty generalizing when the number of training class instances are numerically imbalanced. The problem of generalization in the face of data imbalance has largely been attributed to the lack of training data for
Externí odkaz:
http://arxiv.org/abs/2407.10165
Autor:
Dablain, Damien A., Chawla, Nitesh V.
Data augmentation forms the cornerstone of many modern machine learning training pipelines; yet, the mechanisms by which it works are not clearly understood. Much of the research on data augmentation (DA) has focused on improving existing techniques,
Externí odkaz:
http://arxiv.org/abs/2304.05895
Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compounds the black-box nature of deep networks because the relationships be
Externí odkaz:
http://arxiv.org/abs/2212.07743
Convolutional neural networks (CNNs) have achieved impressive results on imbalanced image data, but they still have difficulty generalizing to minority classes and their decisions are difficult to interpret. These problems are related because the met
Externí odkaz:
http://arxiv.org/abs/2210.09465
Machine learning (ML) is playing an increasingly important role in rendering decisions that affect a broad range of groups in society. ML models inform decisions in criminal justice, the extension of credit in banking, and the hiring practices of cor
Externí odkaz:
http://arxiv.org/abs/2207.06084
Deep learning models tend to memorize training data, which hurts their ability to generalize to under-represented classes. We empirically study a convolutional neural network's internal representation of imbalanced image data and measure the generali
Externí odkaz:
http://arxiv.org/abs/2207.06080
Synthetic biology is an emerging field that involves the engineering and re-design of organisms for purposes such as food security, health, and environmental protection. As such, it poses numerous ethical, legal, and social implications (ELSI) for re
Externí odkaz:
http://arxiv.org/abs/2207.06360
Despite over two decades of progress, imbalanced data is still considered a significant challenge for contemporary machine learning models. Modern advances in deep learning have magnified the importance of the imbalanced data problem. The two main ap
Externí odkaz:
http://arxiv.org/abs/2105.02340
Publikováno v:
Machine Learning; Jul2024, Vol. 113 Issue 7, p4785-4810, 26p
Publikováno v:
Machine Learning; Jun2024, Vol. 113 Issue 6, p3751-3769, 19p