Your browser does not support the audio element. Automatic differentiation is useful for implementing machine learning algorithms such as backpropagation for training ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.

For more than eighty years, deep learning has relied on a simplified model of brain function. The 1943 McCulloch-Pitts model of the neuron fueled breakthroughs in image recognition, speech synthesis ...

Artificial Intelligence systems powered by deep learning are changing how we work, communicate, and make decisions. If we want these technologies to serve society responsibly, tomorrow’s citizens need ...

Books help explain ML in depth, better than short tutorials. The right book depends on goals—coding, theory, or business use. Reading multiple books gives a fuller understanding of machine learning.

In recent years, the exploitation of three-dimensional (3D) data in deep learning has gained momentum despite its inherent challenges. The necessity of 3D approaches arises from the limitations of two ...

This repo attempts to proposes a supervised learning algorithm of SNN by using spike sequences with complex spatio-temporal information. We explore an error back ...

Deep learning (DL) systems have been widely adopted in many industrial and business applications, dramatically improving human productivity, and enabling new industries. However, deep learning has a ...

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. There have been many proposals for DRC-compliant inverse design ...