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Neural networks halve distance measurement errors

The German sensor-maker Leuze claims that it has been able to cut measurement errors in demanding industrial applications by ...
WinBuzzer

Gemma Scope 2: New Google Tools Let Researchers Trace AI ‘Thought’ Circuits

Gemma Scope 2,is a comprehensive open-source suite trained on 110 petabytes of data to map internal reasoning circuits across ...

Interpretable neural networks help reveal the nature of dark matter

A research team from the Xinjiang Astronomical Observatory (XAO) of the Chinese Academy of Sciences has developed an ...
Scientific Research Publishing

Vanishing Gradients in an Artificial Deep Neural Network: The Impact of Activation Functions and Optimization Techniques ()

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 the ...
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20 Activation Functions in Python for Deep Neural Networks – ELU, ReLU, Leaky-ReLU, Sigmoid, Cosine

Explore 20 different activation functions for deep neural networks, with Python examples including ELU, ReLU, Leaky-ReLU, Sigmoid, and more. #ActivationFunctions #DeepLearning #Python Tropical Storm ...
IEEE

FPGA Implementation of Fractional-Order Hopfield Neural Network With Multi-Activation Functions

Abstract: To enable accurate and resource-efficient hardware implementation of fractional-order neural networks for neuromorphic computing, an optimized hardware architecture for field programmable ...
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

Ramachandran, P., Zoph, B. and Le, Q.V. (2017) Searching for Activation Functions. arXiv: 171005941

ABSTRACT: Ordinal outcome neural networks represent an innovative and robust methodology for analyzing high-dimensional health data characterized by ordinal outcomes. This study offers a comparative ...