By using an approximate rather than explicit “kernel” function to extract relationships in very large data sets, KAUST researchers have been able to dramatically accelerate the speed of machine learning. The approach promises to greatly improve the speed of artificial intelligence (AI) in the era of big data. Read More Views: 0
Approximating a kernel of truth Read More »
A research team led by Prof. LI Huiyun from the Shenzhen Institutes of Advanced Technology (SIAT) of the Chinese Academy of Sciences introduced a simple deep reinforcement learning (DRL) algorithm with m-out-of-n bootstrap technique and aggregated multiple deep deterministic policy gradient (DDPG) algorithm structures. Read More Views: 0
Researchers introduce new algorithm to reduce machine learning time Read More »
Demand for electrification of transport has emerged in recent years due to increasing concerns about global warming. The widespread adoption of electric vehicles will result in reduced harmful emissions and cleaner air, among other social and economic benefits. The battery industry is in need of software solutions for battery manufacturers to reduce fabrication and development
Data-driven machine learning is the best approach for advanced battery modelling Read More »
Rice University computer scientists have overcome a major obstacle in the burgeoning artificial intelligence industry by showing it is possible to speed up deep learning technology without specialized acceleration hardware like graphics processing units (GPUs). Read More Views: 0
Deep learning rethink overcomes major obstacle in AI industry Read More »
Scientists from Salk Institute (U.S.), Skoltech (Russia), and the Riken Center for Brain Science (Japan) investigated a theoretical model of how populations of neurons in the visual cortex of the brain may recognize and process faces and their expressions, and how they are organized. The research was recently published in Neural Computation and highlighted on
AI and machine learning help scientists understand human face recognition Read More »
Introductory statistics courses teach us that, when fitting a model to some data, we should have more data than free parameters to avoid the danger of overfitting—fitting noisy data too closely, and thereby failing to fit new data. It is surprising, then, that in modern deep learning the practice is to have orders of magnitude
Why deep networks generalize despite going against statistical intuition Read More »
Researchers at Syracuse University in New York have recently developed a system that can detect the presence of humans in a given environment by analyzing ambient radio frequency (RF) signals. This new system, presented in a paper pre-published on arXiv, employs a convolutional neural network (CNN) trained on a vast amount of RF data. Read
Deep-learning system detects human presence by harvesting RF signals Read More »
Many children with autism face developmental delays, including communication and behavioral challenges and difficulties with social interaction. This makes learning new skills a major challenge, especially in traditional school environments. Read More Views: 0
Socially assistive robot helps children with autism learn Read More »
Now researchers are presenting guidelines for how active matter, such as cells and microorganisms, can best be studied using machine learning techniques. The guidelines can help others navigate the new field, which can significantly improve research in active matter. Read More Views: 0
Shaping the future of machine learning for active matter Read More »
Geneva: France’s Minister for Europe and Foreign Affairs, Mr Jean-Yves Le Drian, and Dr Tedros Adhanom Ghebreyesus, Director-General of WHO today met at the Permanent Mission of France to the United Nations in Geneva to discuss the WHO Academy, which will be the world’s largest and most innovative lifelong learning platform in global health. The
France pledges US$100 million (€90 million) for WHO Academy Read More »