An international team led by Einstein Professor Cecilia Clementi in the Department of Physics at Freie Universität Berlin has ...

Training a machine learning model might sound tricky at first, but it’s actually pretty doable when you break it into steps. Whether you’re working with customer info, photos, or trying ...

We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased. Since a neural network efficiently learns data distribution, a network is ...

The era of predictive modeling enhanced with machine learning and artificial intelligence (AI) to aid clinical ...

The performance of UHPC-CA was predicted in this paper based on five prediction models: multiple linear regression, multiple nonlinear regression, traditional neural network (T-BP), principal ...

Spiking neural networks (SNNs), which are the next generation of artificial neural networks (ANNs), offer a closer mimicry to natural neural networks and hold promise for significant improvements in ...

Despite the recent advances, communication bottleneck still remains as a major challenge against scalability in neural networks. To address this challenge, this paper presents the first scalable multi ...