Mineral wool production is a non-linear process that makes it hard to co...
Deep Learning models have become dominant in tackling financial time-ser...
Bayesian Neural Networks (BNNs) provide a tool to estimate the uncertain...
Research on limit order book markets has been rapidly growing and nowada...
Financial time-series forecasting is one of the most challenging domains...
Multilinear Compressive Learning (MCL) is an efficient signal acquisitio...
Data normalization is one of the most important preprocessing steps when...
Knowledge Distillation refers to a class of methods that transfers the
k...
Recently, the Multilinear Compressive Learning (MCL) framework was propo...
In this paper, we propose 2D-Attention (2DA), a generic attention formul...
Financial time-series analysis and forecasting have been extensively stu...
The recently proposed Multilinear Compressive Learning (MCL) framework
c...
Progressive Neural Network Learning is a class of algorithms that
increm...
Compressive Learning is an emerging topic that combines signal acquisiti...
Forecasting based on financial time-series is a challenging task since m...
Generalized Operational Perceptron (GOP) was proposed to generalize the
...
The traditional Multilayer Perceptron (MLP) using McCulloch-Pitts neuron...
Financial time-series forecasting has long been a challenging problem be...
There has been a great effort to transfer linear discriminant techniques...
The excellent performance of deep neural networks has enabled us to solv...
Nowadays, with the availability of massive amount of trade data collecte...