Mining social media data for biomedical signals and health-related behavior
Social media data have been increasingly used to study biomedical and health-related
phenomena. From cohort-level discussions of a condition to population-level analyses of …
phenomena. From cohort-level discussions of a condition to population-level analyses of …
Anomaly detection based on tiny machine learning: A review
YY Siang, MR Ahamd, MSZ Abidin - Open International Journal of …, 2021 - oiji.utm.my
Anomaly detection (AD) is the detection of patterns in data under expected behavior. In an
industrial environment, any equipment or system that breaks down will affect productivity …
industrial environment, any equipment or system that breaks down will affect productivity …
Sparse persistent RNNs: Squeezing large recurrent networks on-chip
F Zhu, J Pool, M Andersch, J Appleyard… - arXiv preprint arXiv …, 2018 - arxiv.org
Recurrent Neural Networks (RNNs) are powerful tools for solving sequence-based
problems, but their efficacy and execution time are dependent on the size of the network …
problems, but their efficacy and execution time are dependent on the size of the network …
Deep learning weight pruning with rmt-svd: Increasing accuracy and reducing overfitting
Y Shmalo, J Jenkins, O Krupchytskyi - arXiv preprint arXiv:2303.08986, 2023 - arxiv.org
In this work, we present some applications of random matrix theory for the training of deep
neural networks. Recently, random matrix theory (RMT) has been applied to the overfitting …
neural networks. Recently, random matrix theory (RMT) has been applied to the overfitting …
Efficient design of complex-valued neural networks with application to the classification of transient acoustic signals
VS Paul, PA Nelson - The Journal of the Acoustical Society of America, 2024 - pubs.aip.org
A paper by the current authors Paul and Nelson [JASA Express Lett. 3 (9), 094802 (2023)]
showed how the singular value decomposition (SVD) of the matrix of real weights in a neural …
showed how the singular value decomposition (SVD) of the matrix of real weights in a neural …
Enhancing accuracy in deep learning using random matrix theory
L Berlyand, E Sandier, Y Shmalo, L Zhang - arXiv preprint arXiv …, 2023 - arxiv.org
In this study, we explore the applications of random matrix theory (RMT) in the training of
deep neural networks (DNNs), focusing on layer pruning to simplify DNN architecture and …
deep neural networks (DNNs), focusing on layer pruning to simplify DNN architecture and …
Fast training and model compression of gated RNNs via singular value decomposition
Long Short-Term Memory (LSTM) network and Gated Recurrent Units (GRU) network are
two widely-used gated Recurrent Neural Network (RNN) architectures. Both of them usually …
two widely-used gated Recurrent Neural Network (RNN) architectures. Both of them usually …
Matrix analysis for fast learning of neural networks with application to the classification of acoustic spectra
VS Paul, PA Nelson - The Journal of the Acoustical Society of America, 2021 - pubs.aip.org
Neural networks are increasingly being applied to problems in acoustics and audio signal
processing. Large audio datasets are being generated for use in training machine learning …
processing. Large audio datasets are being generated for use in training machine learning …
Data discriminator training method, data discriminator training apparatus, non-transitory computer readable medium, and training method
T Miyato - US Patent 11,593,663, 2023 - Google Patents
(57) APSTRACT A model generation method includes updating, by at least one processor, a
weight matrix of a first neural network model at least based on a first inference result …
weight matrix of a first neural network model at least based on a first inference result …
Artificial intelligent drone-based encrypted machine learning of image extraction using pretrained Convolutional Neural Network (CNN)
M Al Shibli, P Marques, E Spiridon - Proceedings of the 2018 …, 2018 - dl.acm.org
Recently Pretrained Convolutional Neural Networks (CNNs) have proven its effectiveness in
image extraction and classification. This powerful feature of CNNs in image processing is …
image extraction and classification. This powerful feature of CNNs in image processing is …