A comprehensive overview and comparative analysis on deep learning models: CNN, RNN, LSTM, GRU

FM Shiri, T Perumal, N Mustapha… - arxiv preprint arxiv …, 2023 - arxiv.org
Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
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

S Ahmad, I Shakeel, S Mehfuz, J Ahmad - Computer Science Review, 2023 - Elsevier
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 …

Analyzing and improving the training dynamics of diffusion models

T Karras, M Aittala, J Lehtinen… - Proceedings of the …, 2024 - openaccess.thecvf.com
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 …

Gpt-neox-20b: An open-source autoregressive language model

S Black, S Biderman, E Hallahan, Q Anthony… - arxiv preprint arxiv …, 2022 - arxiv.org
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 …

Deep transfer learning approaches for Monkeypox disease diagnosis

MM Ahsan, MR Uddin, MS Ali, MK Islam… - Expert Systems with …, 2023 - Elsevier
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 …

Fixmatch: Simplifying semi-supervised learning with consistency and confidence

K Sohn, D Berthelot, N Carlini… - Advances in neural …, 2020 - proceedings.neurips.cc
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 …

Revisiting weighted aggregation in federated learning with neural networks

Z Li, T Lin, X Shang, C Wu - International Conference on …, 2023 - proceedings.mlr.press
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 …

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

L Alzubaidi, J Zhang, AJ Humaidi, A Al-Dujaili… - Journal of big Data, 2021 - Springer
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 …

Remixmatch: Semi-supervised learning with distribution alignment and augmentation anchoring

D Berthelot, N Carlini, ED Cubuk, A Kurakin… - arxiv preprint arxiv …, 2019 - arxiv.org
We improve the recently-proposed" MixMatch" semi-supervised learning algorithm by
introducing two new techniques: distribution alignment and augmentation anchoring …

Mixmatch: A holistic approach to semi-supervised learning

D Berthelot, N Carlini, I Goodfellow… - Advances in neural …, 2019 - proceedings.neurips.cc
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 …