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61

Comparing the use of all data or specific subsets for training machine learning models in hydrology: A case study of evapotranspiration prediction
Shi, Haiyang ; Luo, Geping ; Hellwich, Olaf ; et al.
In Journal of Hydrology December 2023 627 Part A

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62

Pretrained Deep Learning Models to Reduce Data Needed for Quality Assurance
Paul Hofmann ; Vitaliy Mezhuyev ; Paul Panzitt ; et al.
Proceedings of the 2024 10th International Conference on Computer Technology Applications. :76-85

transfer learning to red... deep learning for qualit... control
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63

Mixtera: A Data Plane for Foundation Model Training
Böther, Maximilian ; Yao, Xiaozhe ; Kerimoglu, Tolga ; et al.

Computer Science - Machi... Computer Science - Artif... Computer Science - Datab...
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64

A comparison of methods for generating synthetic training data for domain adaption of deep learning models in ultrasonic non-destructive evaluation
McKnight, Shaun ; Pierce, S. Gareth ; Mohseni, Ehsan ; et al.
In NDT and E International January 2024 141

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65

Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data
Fooladi, Hosein ; Vu, Thi Ngoc Lan ; Mathea, Miriam ; et al.
Journal of Chemical Information and Modeling. 65(19):9871-9891

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66

Mitigating bias in deep learning: training unbiased models on biased data for the morphological classification of galaxies
Esteban Medina-Rosales ; Guillermo Cabrera-Vives ; Christopher J Miller
Monthly Notices of the Royal Astronomical Society. 531:52-60

Astrophysics of Galaxies... 0103 physical sciences FOS: Physical sciences Astrophysics - Astrophys... 01 natural sciences
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67

ParaPO: Aligning Language Models to Reduce Verbatim Reproduction of Pre-training Data
Chen, Tong ; Brahman, Faeze ; Liu, Jiacheng ; et al.

Computation and Language Artificial Intelligence Machine Learning
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68

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69

Consequences of training data composition for deep learning models in single-cell biology
Ajay Nadig ; Akshaya Thoutam ; Madeline Hughes ; et al.
bioRxiv

Article
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70

Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation
Li, Adrian Shuai ; Bertino, Elisa ; Wu, Rih-Teng ; et al.

Computer Science - Compu... Computer Science - Machi...
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71

Patch-Based Deep-Learning Model With Limited Training Dataset for Liver Tumor Segmentation in Contrast-Enhanced Hepatic Computed Tomography
Yuqiao Yang ; Muneyuki Sato ; Ze Jin ; et al.
IEEE Access, Vol 13, Pp 86863-86873 (2025)

Artificial intelligence small-data AI deep learning Electrical engineering.... liver tumor segmentation medical image analysis …
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72

Mitigating Bias in Deep Learning: Training Unbiased Models on Biased Data for the Morphological Classification of Galaxies
Medina-Rosales, Esteban ; Cabrera-Vives, Guillermo ; Miller, Christopher J.

Astrophysics - Astrophys...
Report
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73

A hybrid stacked ensemble model for rapid seismic damage assessment with imbalanced training data: A case study on the 2023 Kahramanmaraş earthquakes
Mostofi, Sara ; Yilmaz, Zafer ; Başağa, Hasan Basri ; et al.
In Engineering Structures 1 October 2025 340

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74

xRFM: Accurate, scalable, and interpretable feature learning models for tabular data
Beaglehole, Daniel ; Holzmüller, David ; Radhakrishnan, Adityanarayanan ; et al.

Copenhagen, Denmark Supervised learning Kernel methods Tabular data FOS: Computer and inform... Machine Learning (stat.M... …
Konferenz
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76

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77

Resolving quantitative MRI model degeneracy with machine learning via training data distribution design
Guerreri, Michele ; Epstein, Sean ; Azadbakht, Hojjat ; et al.

Physics - Medical Physic... Computer Science - Machi...
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78

Finding a Suitable Class Distribution for Building Histological Images Data Sets Used in Deep Model Training - the Case of Cancer Detection
Reshma, Ismat Ara ; Franchet, Camille ; Gaspard, Margot ; et al.
Journal of Digital Imaging. :1-25

Class-biased training Class distribution analy... Histological image Deep learning Image segmentation and c... Medical information retr... …
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79

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80

How Does the Spatial Distribution of Pre-training Data Affect Geospatial Foundation Models?
Purohit, Mirali ; Muhawenayo, Gedeon ; Rolf, Esther ; et al.

Computer Science - Machi... Computer Science - Compu...
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