Results 11 to 20 of about 17,207,076 (361)
Discovering Parametric Activation Functions [PDF]
Neural Networks, 2022 Recent studies have shown that the choice of activation function can significantly affect the performance of deep learning networks. However, the benefits of novel activation functions have been inconsistent and task dependent, and therefore the rectified linear unit (ReLU) is still the most commonly used.
Garrett Bingham, Risto Miikkulainen
openaire +3 more sources
Reproducing Activation Function for Deep Learning [PDF]
Communications in Mathematical Sciences, 2021 In this paper, we propose the reproducing activation function to improve deep learning accuracy for various applications ranging from computer vision problems to scientific computing problems.
Senwei Liang +3 more
semanticscholar +1 more source
Smish: A Novel Activation Function for Deep Learning Methods
Electronics, 2022 Activation functions are crucial in deep learning networks, given that the nonlinear ability of activation functions endows deep neural networks with real artificial intelligence.
Xueliang Wang, Honge Ren, Achuan Wang
semanticscholar +1 more source
High-Accuracy Detection of Maize Leaf Diseases CNN Based on Multi-Pathway Activation Function Module
Remote Sensing, 2021 Maize leaf disease detection is an essential project in the maize planting stage. This paper proposes the convolutional neural network optimized by a Multi-Activation Function (MAF) module to detect maize leaf disease, aiming to increase the accuracy of ...
Yan Zhang +5 more
semanticscholar +1 more source
Intrusion Detection System Based on Gradient Corrected Online Sequential Extreme Learning Machine
IEEE Access, 2021 Nowadays, Intrusion Detection System (IDS) is an active research topic with machine learning nature. A single-hidden layer feedforward neural network (SLFN) trained on the approach of extreme learning machine (ELM) is used for (IDS).
Nedhal Ahmad Hamdi Qaiwmchi +2 more
doaj +1 more source
Remote Sensing, 2021 Object detection is an important process in surveillance system to locate objects and it is considered as major application in computer vision. The Convolution Neural Network (CNN) based models have been developed by many researchers for object detection
S. N. Shivappriya +4 more
semanticscholar +1 more source
MAIT Cell Activation and Functions [PDF]
Frontiers in Immunology, 2020 Mucosal associated invariant T (MAIT) cells are striking in their abundance and their strict conservation across 150 million years of mammalian evolution, implying they must fulfill critical immunological function(s). MAIT cells are defined by their expression of a semi-invariant αβ TCR which recognizes biosynthetic derivatives of riboflavin synthesis ...
Timothy S. C. Hinks +2 more
openaire +4 more sources
An Adaptive Offset Activation Function for CNN Image Classification Tasks
Electronics, 2022 The performance of the activation function in convolutional neural networks is directly related to the model’s image classification accuracy. The rectified linear unit (ReLU) activation function has been extensively used in image classification models ...
Yuanyuan Jiang, Jinyang Xie, Dong Zhang
semanticscholar +1 more source
Active Identity Function as Activation Function
, 2023 Selecting the optimal activation function for training deep neural networks has always been challenging because it significantly impacts the neural network’s performance and training speed. At this point, researchers are more likely to employ RELU than other well-known activation functions.
Ali Akbar Kiaei +6 more
openaire +1 more source
Active Function Learning [PDF]
, 2018 AbstractHow do people actively explore to learn about functional relationships, that is, how continuous inputs map onto continuous outputs? We introduce a novel paradigm to investigate information search in continuous, multi-feature function learning scenarios.
Jones, Angela +3 more
openaire +4 more sources