The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …

Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …

Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to
model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have …

Deep learning models are vulnerable to adversarial examples. As a more threatening type
for practical deep learning systems, physical adversarial examples have received extensive …

Neuroscience research is undergoing a minor revolution. Recent advances in machine
learning and artificial intelligence research have opened up new ways of thinking about …

Recent work has indicated that, unlike humans, ImageNet-trained CNNs tend to classify
images by texture rather than by shape. How pervasive is this bias, and where does it come …

Non-recurrent deep convolutional neural networks (CNNs) are currently the best at
modeling core object recognition, a behavior that is supported by the densely recurrent …

While successful for various computer vision tasks, deep neural networks have shown to be
vulnerable to texture style shifts and small perturbations to which humans are robust. In this …

We evaluate a wide range of ImageNet models with five trained human labelers. In our year-
long experiment, trained humans first annotated 40,000 images from the ImageNet and …

Deep neural networks (DNNs) are machine learning algorithms that have revolutionized
computer vision due to their remarkable successes in tasks like object classification and …