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A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU
Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …
Deep learning models for cloud, edge, fog, and IoT computing paradigms: Survey, recent advances, and future directions
In recent times, the machine learning (ML) community has recognized the deep learning
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
(DL) computing model as the Gold Standard. DL has gradually become the most widely …
Analyzing and improving the training dynamics of diffusion models
Diffusion models currently dominate the field of data-driven image synthesis with their
unparalleled scaling to large datasets. In this paper we identify and rectify several causes for …
unparalleled scaling to large datasets. In this paper we identify and rectify several causes for …
Gpt-neox-20b: An open-source autoregressive language model
We introduce GPT-NeoX-20B, a 20 billion parameter autoregressive language model
trained on the Pile, whose weights will be made freely and openly available to the public …
trained on the Pile, whose weights will be made freely and openly available to the public …
Deep transfer learning approaches for Monkeypox disease diagnosis
Monkeypox has become a significant global challenge as the number of cases increases
daily. Those infected with the disease often display various skin symptoms and can spread …
daily. Those infected with the disease often display various skin symptoms and can spread …
Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data
to improve a model's performance. This domain has seen fast progress recently, at the cost …
to improve a model's performance. This domain has seen fast progress recently, at the cost …
Revisiting weighted aggregation in federated learning with neural networks
In federated learning (FL), weighted aggregation of local models is conducted to generate a
global model, and the aggregation weights are normalized (the sum of weights is 1) and …
global model, and the aggregation weights are normalized (the sum of weights is 1) and …
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring
We improve the recently-proposed" MixMatch" semi-supervised learning algorithm by
introducing two new techniques: distribution alignment and augmentation anchoring …
introducing two new techniques: distribution alignment and augmentation anchoring …
Mixmatch: A holistic approach to semi-supervised learning
Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled
data to mitigate the reliance on large labeled datasets. In this work, we unify the current …
data to mitigate the reliance on large labeled datasets. In this work, we unify the current …