A simple and clear explanation of stochastic depth — a powerful regularization technique that improves deep neural network ...

Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test accuracy is very low, the model highly overfits the training dataset set ...

Abstract: In graph neural networks (GNNs), both node features and labels are examples of graph signals. While it is common in graph signal processing to impose signal smoothness constraints in ...

Neural processing unts (NPUs) are the latest chips you might find in smartphones and laptops — but what are they ard why are they so important? When you purchase through links on our site, we may earn ...

ABSTRACT: Pneumonia remains a significant cause of morbidity and mortality worldwide, particularly in vulnerable populations such as children and the elderly. Early detection through chest X-ray ...

ABSTRACT: Delirium is a common yet critical condition among Intensive Care Unit (ICU) patients, characterized by acute cognitive disturbances that can lead to severe complications, prolonged hospital ...

Abstract: Incremental randomized neural networks have been widely applied in industrial data modeling. However, incremental randomized neural networks may generate redundant hidden nodes. These nodes ...

Artificial intelligence might now be solving advanced math, performing complex reasoning, and even using personal computers, but today’s algorithms could still learn a thing or two from microscopic ...

subword 分割には任意性があり（hello という文字列をどう分割するかはいくつかのパターンがある。もちろん出現頻度などでどの分割がよさそうかは一定判断できるが、そこには単語分割など ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.