Convolutional Neural Networks (CNN) are a class of machine learning models
predominately used in computer vision tasks and can achieve human-like performance …

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 …

Recent advances in neural network modeling have enabled major strides in computer vision
and other artificial intelligence applications. Human-level visual recognition abilities are …

Imagine a world where machines can see and understand the world the way humans do.
Rapid progress in artificial intelligence has led to smartphones that recognize faces, cars …

In this paper we consider recent advances in the use of deep convolutional neural networks
to understanding biological vision. We focus on claims about the plausibility of feedforward …

Convolutional neural networks (CNNs) have achieved very high object categorization
performance recently. It has increasingly become a common practice in human fMRI …

How do we recognize objects despite changes in their appearance? The past three decades
have been witness to intense debates regarding both whether objects are encoded …

Convolutional neural networks trained on object recognition derive inspiration from the
neural architecture of the visual system in mammals, and have been used as models of the …

Artificial vision has often been described as one of the key remaining challenges to be
solved before machines can act intelligently. Recent developments in a branch of machine …

Deep convolutional neural networks (CNNs) have certain structural, mechanistic,
representational, and functional parallels with primate visual cortex and also many …