Results 31 to 40 of about 8,885,948 (291)
Subgraph-aware Few-Shot Inductive Link Prediction via Meta-Learning
, 2022 Link prediction for knowledge graphs aims to predict missing connections between entities. Prevailing methods are limited to a transductive setting and hard to process unseen entities.
Mai, S, Zheng, S, Yang, Y, Hu, H, Sun, Y
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Unsupervised Few-Shot Feature Learning via Self-Supervised Training
Frontiers in Computational Neuroscience, 2020 Learning from limited exemplars (few-shot learning) is a fundamental, unsolved problem that has been laboriously explored in the machine learning community.
Zilong Ji +4 more
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Feature Transformation Network for Few-Shot Learning
IEEE Access, 2021 Few-shot learning researches to learn a novel concept from a handful of labeled samples. Due to the small amount of training data, deep network has the risk of over-fitting. Although many previous approaches based on metric criterion can make significant
Xiaoyan Wang, Hongmei Wang, Daming Zhou
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Few-Shot Learning for Low-Data Drug Discovery
, 2022 The discovery of new hits through ligand-based virtual screening in drug discovery is essentially a low-data problem, as data acquisition is both difficult and expensive.
Jean-Paul Ebejer (1747735) +1 more
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Few-shot learning-based network intrusion detection through an enhanced parallelized triplet network
, 2022 Network intrusion detection is one of the critical techniques to enhance cybersecurity. Several few-shot learning-based methods have recently been proposed to alleviate the dependence on large training samples in many supervised learning methods. However,
Jing Qin (41397) +6 more
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Applying a Probabilistic Network Method to Solve Business-Related Few-Shot Classification Problems
Complexity, 2021 It can be challenging to learn algorithms due to the research of business-related few-shot classification problems. Therefore, in this paper, we evaluate the classification of few-shot learning in the commercial field.
Lang Wu, Menggang Li
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Few-shot Learning: Methods and Applications [PDF]
ITM Web of ConferencesThe Few-shot learning (FSL) approach distills meaningful features from a constrained sample set, allowing models to swiftly adjust to novel tasks and decreasing the dependency on extensive datasets. This approach leverages methods involving meta-learning,
Li Jiaxiang, Li Mingyang
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Active Few-Shot Learning with FASL
, 2022 Recent advances in natural language processing (NLP) have led to strong text classification models for many tasks. However, still often thousands of examples are needed to train models with good quality. This makes it challenging to quickly develop and deploy new models for real world problems and business needs.
Thomas Müller 0009 +3 more
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Improving Augmentation Efficiency for Few-Shot Learning
IEEE Access, 2022 While human intelligence can easily recognize some characteristics of classes with one or few examples, learning from few examples is a challenging task in machine learning.
Wonhee Cho, Eunwoo Kim
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Meta learning for supervised and unsupervised few-shot learning [PDF]
, 2021 Meta-learning or learning-to-learn involves automatically learning training-algorithms such that models trained with such learnt algorithms can solve a number of tasks while demonstrating high performance in a number of predefined objectives.
Antoniou, Antreas
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