Convolutional neural networks (CNNs) were inspired by early findings in the study of
biological vision. They have since become successful tools in computer vision and state-of …

In recent years, molecular deep generative models have attracted much attention for its
application in de novo drug design. The data-driven molecular deep generative model …

Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by
learning increasingly complex representations of object shapes. Some recent studies …

We compare the robustness of humans and current convolutional deep neural networks
(DNNs) on object recognition under twelve different types of image degradations. First, using …

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 …

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 …

Abstract Machine learning models are vulnerable to adversarial examples: small changes to
images can cause computer vision models to make mistakes such as identifying a school …

Deep neural networks provide the current best models of visual information processing in
the primate brain. Drawing on work from computer vision, the most commonly used networks …

Abstract Convolutional Neural Networks (CNNs) are known to rely more on local texture
rather than global shape when making decisions. Recent work also indicates a close …

Artificial intelligence models are becoming an integral part of modern computing systems.
Just like software inevitably has bugs, models have bugs too, leading to poor classification …