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What are the ongoing practices in Machine Learning and Deep Learning Applications in Non-Linear Predictive Modelling? 


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Machine learning and deep learning techniques are being used in non-linear predictive modeling. One approach is to combine different methods to improve prediction accuracy. For example, a study by Oliveira et al. proposes a model that combines Complete Ensemble EMD with Adaptive Noise, Singular Value Decomposition, and Long Short Term Memory network to predict non-linear and non-stationary time series data . Another approach is to use deep learning models to emulate simulations of structure formation in cosmology. A model developed by Oliveira et al. uses a V-Net based model to transform fast linear predictions into fully non-linear predictions from numerical simulations . Additionally, Bayesian generalized nonlinear regression models with a comprehensive non-linear feature space have been introduced. These models generate non-linear features hierarchically, similar to deep learning, and allow for variable selection, resulting in more interpretable models . Deep learning estimation has also been applied to non-linear factor models, providing improved modeling flexibility and estimation accuracy . Overall, these studies demonstrate the ongoing practices in machine learning and deep learning applications in non-linear predictive modeling.

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The provided paper discusses the implementation of non-linear manifold reduced-order models using convolutional autoencoders and reduced over-collocation method. It does not specifically mention ongoing practices in machine learning and deep learning applications in non-linear predictive modeling.
The provided paper discusses the use of deep learning estimation in non-linear factor models and its improvements in modeling flexibility compared to traditional factor models. However, it does not specifically mention ongoing practices in machine learning and deep learning applications in non-linear predictive modeling.
The paper discusses the use of Bayesian generalized nonlinear regression models with a comprehensive non-linear feature space for non-linear predictive modeling. It also compares the predictive performance with other machine learning algorithms.
The provided paper discusses the use of a V-Net based model in non-linear predictive modeling for cosmological simulations. It does not provide information about ongoing practices in machine learning and deep learning applications in non-linear predictive modeling.

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