DeepCSO: Forecasting of Combined Sewer Overflow at a Citywide Level using Multi-task Deep Learning

Keywords: deep learning; Bayesian convolutional neural network; uncertainty quantification; time series classification; CO 2 -leak

The two networks were tested for the lung segmentation task, using different combinations of deep learning frameworks (e.g., Tensorflow and PyTorch), training precision, batch sizes

The development of a deep learning method for detecting hate speech based on anomaly detection techniques, using semi-supervised learning, pre-trained word embeddings and

As a prominent result, deep learning methods (RNN and LSTM) show a technical skill to forecast stock movement in both approaches, especially for continuous data when the performance

Men samtidig så ser jeg jo at hvis vi ser på dette med lesing i alle fag, og regning, altså det...noe av dette er kanskje dybdelæring litt av tanken, at man for eksempel skal

While the goal of the traditional machine learning classification task is to train a classifier that is able to handle out-of-sample data points, e.g., tasks such as the

This subsection represents an inverse of the previous task and it is also more challenging. Here, the rate is predicted based on time and pressure, using only the LSTM neural

Problem Queue Congestion Flow Prediction Forecasting Estimation Techniques Deep learning Neural networks Machine learning Big data.. Second step is to select the