Zobrazeno 1 - 10
of 361
pro vyhledávání: '"Verma, Shekhar"'
The rainfall associated with Topical Cyclone(TC) contributes a major amount to the annual rainfall in India. Due to the limited research on the quantitative precipitation associated with Tropical Cyclones (TC), the prediction of the amount of precipi
Externí odkaz:
http://arxiv.org/abs/2407.05255
The reason for Meta Overfitting can be attributed to two factors: Mutual Non-exclusivity and the Lack of diversity, consequent to which a single global function can fit the support set data of all the meta-training tasks and fail to generalize to new
Externí odkaz:
http://arxiv.org/abs/2405.12299
In Federated Learning, model training is performed across multiple computing devices, where only parameters are shared with a common central server without exchanging their data instances. This strategy assumes abundance of resources on individual cl
Externí odkaz:
http://arxiv.org/abs/2309.00864
In this work, we propose a fast adaptive federated meta-learning (FAM) framework for collaboratively learning a single global model, which can then be personalized locally on individual clients. Federated learning enables multiple clients to collabor
Externí odkaz:
http://arxiv.org/abs/2308.13970
Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecast
Externí odkaz:
http://arxiv.org/abs/2308.10995
Meta-learning aims to solve unseen tasks with few labelled instances. Nevertheless, despite its effectiveness for quick learning in existing optimization-based methods, it has several flaws. Inconsequential connections are frequently seen during meta
Externí odkaz:
http://arxiv.org/abs/2304.02862
It is essential to classify brain tumors from magnetic resonance imaging (MRI) accurately for better and timely treatment of the patients. In this paper, we propose a hybrid model, using VGG along with Nonlinear-SVM (Soft and Hard) to classify the br
Externí odkaz:
http://arxiv.org/abs/2212.02794
Prototypical network for Few shot learning tries to learn an embedding function in the encoder that embeds images with similar features close to one another in the embedding space. However, in this process, the support set samples for a task are embe
Externí odkaz:
http://arxiv.org/abs/2211.12479
Publikováno v:
In Pattern Recognition Letters October 2024 186:205-212
