IEEE

Harris Performance as Activation Function for Image Classification in Convolutional Neural Network and Transfer Learning Models

Deep neural networks (DNN) presents various applications in many industries and addresses numerous real-world problems. [1] The goal of the DNNs is to mimic the biological neuron of the human mind and ...
Scientific Research Publishing

Pedamonti, D. (2018) Comparison of Non-Linear Activation Functions for Deep Neural Networks on MNIST Classification Task. arXiv.

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
Hosted on MSN

What Are Activation Functions in Deep Learning? Explained Clearly

Understand what activation functions are and why they’re essential in deep learning! This beginner-friendly explanation covers popular functions like ReLU, Sigmoid, and Tanh—showing how they help ...
Frontiers

CDPNet: a deformable ProtoPNet for interpretable wheat leaf disease identification

1 College of Information and Network Engineering, Anhui Science and Technology University, Bengbu, Anhui, China 2 State Key Laboratory of Media Convergence and Communication, Communication University ...
GitHub

Skieriya/Pi-Activation-Function

We introduce Π-Activation (pronounced "pi-activation"), a smooth hybrid non-linearity that combines a logarithmic–ReLU branch with a gated linear pathway. The function is positive-homogeneous for ...
IEEE

Skew Logistic Distribution Applied as Activation Function in Artificial Neural Networks

Abstract: In recent years, Artificial Neural Networks (ANNs) have stood out among machine learning algorithms in many applications, such as image and video pattern recognition. Activation functions ...
Scientific Research Publishing

Leshno, M., Lin, V.Y., Pinkus, A. and Schocken, S. (1993) Multilayer Feedforward Networks with a Nonpolynomial Activation Function Can Approximate Any Function. Neural Networks ...

ABSTRACT: We explore the performance of various artificial neural network architectures, including a multilayer perceptron (MLP), Kolmogorov-Arnold network (KAN), LSTM-GRU hybrid recursive neural ...
Semiconductor Engineering

Implementing AI Activation Functions

Activation functions play a critical role in AI inference, helping to ferret out nonlinear behaviors in AI models. This makes them an integral part of any neural network, but nonlinear functions can ...