In the recent past, more than 5 years or so, Deep Learning (DL) especially the large
language models (LLMs) has generated extensive studies out of a distinctly average …

Smarter applications are making better use of the insights gleaned from data, having an
impact on every industry and research discipline. At the core of this revolution lies the tools …

Deep learning frameworks have often focused on either usability or speed, but not both.
PyTorch is a machine learning library that shows that these two goals are in fact compatible …

This paper presents the winning submission of the M4 forecasting competition. The
submission utilizes a dynamic computational graph neural network system that enables a …

This paper describes AllenNLP, a platform for research on deep learning methods in natural
language understanding. AllenNLP is designed to support researchers who want to build …

Neural ranking models for information retrieval (IR) use shallow or deep neural networks to
rank search results in response to a query. Traditional learning to rank models employ …

Multi-task learning (MTL) allows deep neural networks to learn from related tasks by sharing
parameters with other networks. In practice, however, MTL involves searching an enormous …

Deep learning (DL), as one of the most active machine learning research fields, has
achieved great success in numerous scientific and technological disciplines, including …

Bullying is described as an undesirable behavior by others that harms an individual
physically, mentally, or socially. Cyberbullying is a virtual form (eg, textual or image) of …

This work presents a hybrid and hierarchical deep learning model for midterm load
forecasting. The model combines exponential smoothing (ETS), advanced long short-term …