Results 11 to 20 of about 10,630,553 (293)
Small- and medium-enterprises bankruptcy dataset
Data in Brief, 2019 Bankruptcy prediction is a long-standing issue that receives significant attention of academic researchers and industry practitioners. Most of the papers on bankruptcy prediction focus on companies that are listed on the stock market, and there are only ...
Peter Drotár +3 more
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Optimizing Deeper Transformers on Small Datasets [PDF]
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021 It is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layers on top of pre-trained models during fine-tuning. This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of
Peng Xu +8 more
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Dataset for small object detection with shadow (SODwS)Mendeley Data
Data in BriefDetecting small objects in aerial images poses several challenges, including issues with resolution limitations, scale variability, background clutter, and object occlusion.
Shahbe Mat-Desa +8 more
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Hebbian dreaming for small datasets
Neural Networks, 2023 The dreaming Hopfield model constitutes a generalization of the Hebbian paradigm for neural networks, that is able to perform on-line learning when "awake" and also to account for off-line "sleeping" mechanisms. The latter have been shown to enhance storing in such a way that, in the long sleep-time limit, this model can reach the maximal storage ...
Agliari, Elena +5 more
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Transformers Meet Small Datasets
IEEE Access, 2022 The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be trained directly on small datasets.
Ran Shao, Xiaojun Bi 0002
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Federated Learning from Small Datasets
CoRR, 2021 Federated learning allows multiple parties to collaboratively train a joint model without sharing local data. This enables applications of machine learning in settings of inherently distributed, undisclosable data such as in the medical domain. In practice, joint training is usually achieved by aggregating local models, for which local training ...
Kamp, Michael +2 more
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Vision Transformer for Small-Size Datasets
CoRR, 2021 Recently, the Vision Transformer (ViT), which applied the transformer structure to the image classification task, has outperformed convolutional neural networks. However, the high performance of the ViT results from pre-training using a large-size dataset such as JFT-300M, and its dependence on a large dataset is interpreted as due to low locality ...
Seung Hoon Lee +2 more
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Neural Network Modeling for Small Datasets [PDF]
Technometrics, 2005 Neural network modeling for small datasets can be justified from a theoretical point of view according to some of Bartlett's results showing that the generalization performance of a multilayer perc...
Salvatore Ingrassia, Isabella Morlini
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Small-scale dataset comparative analysis using computation resources.
, 2023 Small-scale dataset comparative analysis using computation resources.
Olaide N. Oyelade (14047002) +2 more
core +1 more source
Retinal Image Segmentation with Small Datasets
Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies, 2023 Many eye diseases like Diabetic Macular Edema (DME), Age-related Macular Degeneration (AMD), and Glaucoma manifest in the retina, can cause irreversible blindness or severely impair the central version. The Optical Coherence Tomography (OCT), a 3D scan of the retina with high qualitative information about the retinal morphology, can be used to diagnose
Ndipenoch, N, Miron, A, Wang, Z, Li, Y
openaire +5 more sources