Tag Neural and Evolutionary Computing
What is Temporal Ensembling? Temporal Ensembling, a novel approach in the realm of semi-supervised learning, has recently garnered attention for its ability to deliver exceptional results. The method works by maintaining an exponential moving average of label predictions for each… Continue Reading →
Delving into the intricate world of deep learning, researchers have long grappled with the persistent challenge of vanishing and exploding gradients. While solutions like meticulous initializations and batch normalization have alleviated this hurdle to some extent, architectures embedding skip-connections, such… Continue Reading →
Convolutional neural networks (CNNs) have proven to be highly effective in various domains, including computer vision, natural language processing, and speech recognition. However, training these networks can be a time-consuming and resource-intensive process. The need for faster and more efficient… Continue Reading →
Large feedforward neural networks have become increasingly popular over the years due to their ability to learn complex patterns and make accurate predictions. However, a common challenge with these networks is their poor performance on test data, a phenomenon known… Continue Reading →