The demand for artificial intelligence has grown significantly over the past decade, and this
growth has been fueled by advances in machine learning techniques and the ability to …

Deep learning, a state-of-the-art machine learning approach, has shown outstanding
performance over traditional machine learning in identifying intricate structures in complex …

Deep learning (DL) techniques have obtained remarkable achievements on various tasks,
such as image recognition, object detection, and language modeling. However, building a …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

Over the last years deep learning methods have been shown to outperform previous state‐of‐
the‐art machine learning techniques in several fields, with computer vision being one of the …

The translation equivariance of convolutional layers enables CNNs to generalize well on
image problems. While translation equivariance provides a powerful inductive bias for …

Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

Evolutionary paradigms have been successfully applied to neural network designs for two
decades. Unfortunately, these methods cannot scale well to the modern deep neural …

Many researchers have sought ways of model compression to reduce the size of a deep
neural network (DNN) with minimal performance degradation in order to use DNNs in …

Deep neural networks have been shown to outperform classical machine learning
algorithms in solving real-world problems. However, the most successful deep neural …