Results 51 to 60 of about 252,636 (309)
Gaussian noise up-sampling is better suited than SMOTE and ADASYN for clinical decision making
BioData Mining, 2021 Clinical data sets have very special properties and suffer from many caveats in machine learning. They typically show a high-class imbalance, have a small number of samples and a large number of parameters, and have missing values.
Jacqueline Beinecke, Dominik Heider
doaj +1 more source
Implicit Semantic Data Augmentation for Hand Pose Estimation
IEEE Access, 2022 Data augmentation is a well-known technique used for improving the generalization performance of modern neural networks. After the success of several traditional random data augmentation for images (including flipping, translation, or rotation), a recent
Kyeongeun Seo +3 more
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An adaptive fusion-based data augmentation method for abstract dialogue summarization [PDF]
PeerJ Computer ScienceThe dialogue summarization is necessary for information retrieval, and the training of abstract dialogue summarization models heavily rely on large amounts of labeled data.
Weihao Li +4 more
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Data Augmentation using Counterfactuals: Proximity vs Diversity
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2022 Counterfactual explanations are gaining in popularity as a way of explaining machine learning models. Counterfactual examples are generally created to help interpret the decision of a model.
Md Golam Moula Mehedi Hasan +1 more
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Empirical copula-based data augmentation for mixed-type datasets: a robust approach for synthetic data generation [PDF]
PeerJ Computer ScienceData augmentation is a critical technique for enhancing model performance in scenarios with limited, sparse, or imbalanced datasets. While existing methods often focus on homogeneous data types (e.g., continuous-only or categorical-only), real-world ...
Mohsen Ben Hassine, Lamine Mili
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A New Multispectral Data Augmentation Technique Based on Data Imputation
Remote Sensing, 2021 Deep Learning (DL) has been recently introduced into the hyperspectral and multispectral image classification landscape. Despite the success of DL in the remote sensing field, DL models are computationally intensive due to the large number of parameters ...
Álvaro Acción +2 more
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AugLy: Data Augmentations for Robustness
CoRR, 2022 We introduce AugLy, a data augmentation library with a focus on adversarial robustness. AugLy provides a wide array of augmentations for multiple modalities (audio, image, text, & video). These augmentations were inspired by those that real users perform on social media platforms, some of which were not already supported by existing data ...
Zoe Papakipos, Joanna Bitton
openaire +2 more sources
Protein pyrophosphorylation by inositol pyrophosphates — detection, function, and regulation
FEBS Letters, EarlyView.Protein pyrophosphorylation is an unusual signaling mechanism that was discovered two decades ago. It can be driven by inositol pyrophosphate messengers and influences various cellular processes. Herein, we summarize the research progress and challenges of this field, covering pathways found to be regulated by this posttranslational modification as ...
Sarah Lampe +3 more
wiley +1 more source
, 2020 : Effective and rapid deep learning method to predict chemical reactions contributes to the research and development of organic chemistry and drug discovery.
Yun, Zhang +7 more
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The ubiquitin ligase RNF115 is required for the clearance of damaged lysosomes
FEBS Letters, EarlyView.Upon lysosomal rupture, an E3 ubiquitin ligase RNF115 translocates from the cytosol to the damaged lysosomal membrane. Moreover, RNF115 depletion impairs the clearance of damaged lysosomes, identifying it as a key regulator of lysosomal quality control.
Sae Nakanaga +3 more
wiley +1 more source