Despite advancements, AI has yet to achieve continuous, lifelong learning and the ability to generalize from experience.

While deepfake generation advances in complexity, it also becomes more computationally demanding. In contrast, detection is learning to do more with less.

Traditional machine learning methods have often struggled to process such data in an effective manner. Graph Neural Networks represent a crucial advance in the use of deep learning to interpret and ...

A new editorial was published in Oncotarget, Volume 16, on April 4, 2025, titled “Deep learning-based uncertainty quantification for quality assurance in hepatobiliary imaging-based techniques.” ...

Contributions may include studies on energy-efficient neural network models for robotic systems, strategies to enhance robotic resilience in dynamic, real-world settings, or novel neural network ...

Using these datasets, 72 models with varying configurations – including different convolutional neural network architectures ... also indicating that classic and GAN-based augmentation approaches had ...

Generative adversarial networks (GAN) are a class of generative machine learning frameworks. A GAN consists of two competing neural networks, often termed the Discriminator network and the Generator ...

With the appearance of gallium nitride (GaN), this task has even become more complex, because the electrical properties of GaN expand the room for possible system solutions. Particularly the lack of a ...

A new technical paper titled “Thermal Boundary Resistance Reduction by Interfacial Nanopatterning for GaN-on-Diamond Electronics Applications” was published by researchers at University of Bristol, ...