Application of supervised machine learning paradigms in the prediction of petroleum reservoir properties: Comparative analysis of ANN and SVM models
Abstract The advent of Artificial Intelligence (AI) in the petroleum industry has seen an
increase in its use in exploration, development, production, reservoir engineering and …
increase in its use in exploration, development, production, reservoir engineering and …
A review on weight initialization strategies for neural networks
Over the past few years, neural networks have exhibited remarkable results for various
applications in machine learning and computer vision. Weight initialization is a significant …
applications in machine learning and computer vision. Weight initialization is a significant …
Dataset condensation with distribution matching
Computational cost of training state-of-the-art deep models in many learning problems is
rapidly increasing due to more sophisticated models and larger datasets. A recent promising …
rapidly increasing due to more sophisticated models and larger datasets. A recent promising …
Improved distribution matching for dataset condensation
Dataset Condensation aims to condense a large dataset into a smaller one while
maintaining its ability to train a well-performing model, thus reducing the storage cost and …
maintaining its ability to train a well-performing model, thus reducing the storage cost and …
Datadam: Efficient dataset distillation with attention matching
Researchers have long tried to minimize training costs in deep learning while maintaining
strong generalization across diverse datasets. Emerging research on dataset distillation …
strong generalization across diverse datasets. Emerging research on dataset distillation …
Unsupervised pre-training of a deep LSTM-based stacked autoencoder for multivariate time series forecasting problems
Currently, most real-world time series datasets are multivariate and are rich in dynamical
information of the underlying system. Such datasets are attracting much attention; therefore …
information of the underlying system. Such datasets are attracting much attention; therefore …
Dual stream network with attention mechanism for photovoltaic power forecasting
The operations of renewable power generation systems highly depend on precise
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
Non-iterative and fast deep learning: Multilayer extreme learning machines
In the past decade, deep learning techniques have powered many aspects of our daily life,
and drawn ever-increasing research interests. However, conventional deep learning …
and drawn ever-increasing research interests. However, conventional deep learning …
Machine learning and integrative analysis of biomedical big data
Recent developments in high-throughput technologies have accelerated the accumulation
of massive amounts of omics data from multiple sources: genome, epigenome …
of massive amounts of omics data from multiple sources: genome, epigenome …
Broad learning system with locality sensitive discriminant analysis for hyperspectral image classification
In this paper, we propose a new method for hyperspectral images (HSI) classification,
aiming to take advantage of both manifold learning‐based feature extraction and neural …
aiming to take advantage of both manifold learning‐based feature extraction and neural …