This paper proposes a novel deep learning ... Transformer-based attention mechanisms to capture both short-term and long-term dependencies in time-series sales data. Utilizing the Favorita Grocery ...

While deep learning ... fields. Transformer-based methods, on the other hand, suffer from quadratic complexity when handling long-range dependencies. Methods: To overcome these limitations, we ...

Methods: In light of this, this paper proposes a hybrid deep learning model based on Transformer-Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) to improve the accuracy of climate ...

How transformers work, why they are so important for the growth of scalable solutions and why they are the backbone of LLMs.

For the retrospective study, an ensemble 3D U-Net deep learning model was trained for lung tumor detection and segmentation using 1,504 CT scans with 1,828 segmented lung tumors. The model was ...

The advantage of deep learning is that the program builds the feature set all by itself through unsupervised learning. For instance, the model is fed with training data made up of labeled images ...

A pragmatic response to the AI-race is an investment in double literacy, to curate hybrid literacy, the complementary magic ...

The reasoning model is designed to be much more competent in coding, math, and science. OpenAI also launched Deep Research, which is meant to handle complex, multi-step research and which will ...