Activation functions in deep learning: A comprehensive survey and benchmark
Neural networks have shown tremendous growth in recent years to solve numerous
problems. Various types of neural networks have been introduced to deal with different types …
problems. Various types of neural networks have been introduced to deal with different types …
Deep convolutional neural networks for image classification: A comprehensive review
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 …
1980s. However, despite a few scattered applications, they were dormant until the mid …
CSPNet: A new backbone that can enhance learning capability of CNN
Neural networks have enabled state-of-the-art approaches to achieve incredible results on
computer vision tasks such as object detection. However, such success greatly relies on …
computer vision tasks such as object detection. However, such success greatly relies on …
[BOOK][B] Neural networks and deep learning
CC Aggarwal - 2018 - Springer
“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 …
McDonald Neural networks were developed to simulate the human nervous system for …
Searching for activation functions
The choice of activation functions in deep networks has a significant effect on the training
dynamics and task performance. Currently, the most successful and widely-used activation …
dynamics and task performance. Currently, the most successful and widely-used activation …
Activation functions: Comparison of trends in practice and research for deep learning
Deep neural networks have been successfully used in diverse emerging domains to solve
real world complex problems with may more deep learning (DL) architectures, being …
real world complex problems with may more deep learning (DL) architectures, being …
Densely connected convolutional networks
Recent work has shown that convolutional networks can be substantially deeper, more
accurate, and efficient to train if they contain shorter connections between layers close to the …
accurate, and efficient to train if they contain shorter connections between layers close to the …
V2x-vit: Vehicle-to-everything cooperative perception with vision transformer
In this paper, we investigate the application of Vehicle-to-Everything (V2X) communication to
improve the perception performance of autonomous vehicles. We present a robust …
improve the perception performance of autonomous vehicles. We present a robust …
Learning efficient convolutional networks through network slimming
The deployment of deep convolutional neural networks (CNNs) in many real world
applications is largely hindered by their high computational cost. In this paper, we propose a …
applications is largely hindered by their high computational cost. In this paper, we propose a …
Geometric deep learning: going beyond euclidean data
Geometric deep learning is an umbrella term for emerging techniques attempting to
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …