Emotion estimation is a field that has been studied for a long time, and several approaches using machine learning models exist. This article presents BlendFER-Lite, an LSTM model that uses ...
Regularizing and Optimizing LSTM Language Models An Analysis of Neural Language Modeling at Multiple Scales This code was originally forked from the PyTorch word level language modeling example. The ...
Time-series data in manufacturing (temperature, pressure, vibration, current...) is tricky. Data preprocessing, windowing, normalization, the format to pass to the model... "I'll visualize that data ...
@article{cornia2018predicting, author = {Cornia, Marcella and Baraldi, Lorenzo and Serra, Giuseppe and Cucchiara, Rita}, title = {{Predicting Human Eye Fixations via an LSTM-based Saliency Attentive ...
Current electrocardiogram (ECG) criteria of left ventricular hypertrophy (LVH) have low sensitivity. Deep learning (DL) techniques have been widely used to detect cardiac diseases due to its ability ...
LSTM networks are designed to overcome long-term dependency issues in sequential data. RNNs utilise memory from previous inputs to affect current outputs, unlike traditional neural networks. The main ...
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