Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …

The difficulty of deploying various deep learning (DL) models on diverse DL hardware has
boosted the research and development of DL compilers in the community. Several DL …

Video prediction is a challenging task. The quality of video frames from current state-of-the-
art (SOTA) generative models tends to be poor and generalization beyond the training data …

This work presents Kornia--an open source computer vision library which consists of a set of
differentiable routines and modules to solve generic computer vision problems. At its core …

Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

Graph Neural Networks (graph NNs) are a promising deep learning approach for analyzing
graph-structured data. However, it is known that they do not improve (or sometimes worsen) …

The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast
communication protocols, and efficient cybersecurity mechanisms to improve industrial …

This paper empirically evaluates the several machine learning algorithms adaptable for lung
cancer detection linked with IoT devices. In this work, a review of nearly 65 papers for …

Predicting outcomes and planning interactions with the physical world are long-standing
goals for machine learning. A variety of such tasks involves continuous physical systems …

Tensor computations underlie modern scientific computing and deep learning. A number of
tensor frameworks emerged varying in execution model, hardware support, memory …