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 …
A survey on modern trainable activation functions
In neural networks literature, there is a strong interest in identifying and defining activation
functions which can improve neural network performance. In recent years there has been a …
functions which can improve neural network performance. In recent years there has been a …
How important are activation functions in regression and classification? A survey, performance comparison, and future directions
AD Jagtap, GE Karniadakis - Journal of Machine Learning for …, 2023 - dl.begellhouse.com
Inspired by biological neurons, the activation functions play an essential part in the learning
process of any artificial neural network (ANN) commonly used in many real-world problems …
process of any artificial neural network (ANN) commonly used in many real-world problems …
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Recent work has shown that optical flow estimation can be formulated as a supervised
learning task and can be successfully solved with convolutional networks. Training of the so …
learning task and can be successfully solved with convolutional networks. Training of the so …
Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challenges
Breast cancer is a common and fatal disease among women worldwide. Therefore, the early
and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of …
and precise diagnosis of breast cancer plays a pivotal role to improve the prognosis of …
Deep neural network approximation for custom hardware: Where we've been, where we're going
Deep neural networks have proven to be particularly effective in visual and audio
recognition tasks. Existing models tend to be computationally expensive and memory …
recognition tasks. Existing models tend to be computationally expensive and memory …
Practical options for selecting data-driven or physics-based prognostics algorithms with reviews
This paper is to provide practical options for prognostics so that beginners can select
appropriate methods for their fields of application. To achieve this goal, several popular …
appropriate methods for their fields of application. To achieve this goal, several popular …
Integrating metaheuristics and artificial neural networks for improved stock price prediction
Stock market price is one of the most important indicators of a country's economic growth.
That's why determining the exact movements of stock market price is considerably regarded …
That's why determining the exact movements of stock market price is considerably regarded …
A hybrid recommender system using artificial neural networks
TK Paradarami, ND Bastian, JL Wightman - Expert Systems with …, 2017 - Elsevier
In the context of recommendation systems, metadata information from reviews written for
businesses has rarely been considered in traditional systems developed using content …
businesses has rarely been considered in traditional systems developed using content …
Application of artificial intelligence in stock market forecasting: a critique, review, and research agenda
The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal
and external environmental variables. Artificial intelligence (AI) techniques can detect such …
and external environmental variables. Artificial intelligence (AI) techniques can detect such …