Integration of photovoltaics into power grids is difficult as solar energy is highly dependent
on climate and geography; often fluctuating erratically. This causes penetrations and voltage …

Object detection, as of one the most fundamental and challenging problems in computer
vision, has received great attention in recent years. Over the past two decades, we have …

We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by
recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few …

Long-term time series forecasting is challenging since prediction accuracy tends to
decrease dramatically with the increasing horizon. Although Transformer-based methods …

We show that Transformer encoder architectures can be sped up, with limited accuracy
costs, by replacing the self-attention sublayers with simple linear transformations that" mix" …

Self-attention has the promise of improving computer vision systems due to parameter-
independent scaling of receptive fields and content-dependent interactions, in contrast to …

Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

The classical development of neural networks has primarily focused on learning mappings
between finite-dimensional Euclidean spaces. Recently, this has been generalized to neural …

Transformers have been actively studied for time-series forecasting in recent years. While
often showing promising results in various scenarios, traditional Transformers are not …

Nowadays, Deep Neural Networks are among the main tools used in various sciences.
Convolutional Neural Network is a special type of DNN consisting of several convolution …