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 …

Neuroscience has focused on the detailed implementation of computation, studying neural
codes, dynamics and circuits. In machine learning, however, artificial neural networks tend …

In the cognitive, computational, and neuro-sciences, practitioners often reason about what
computational models represent or learn, as well as what algorithm is instantiated. The …

Backpropagation of error (backprop) is a powerful algorithm for training machine learning
architectures through end-to-end differentiation. Recently it has been shown that backprop …

Recognizing the people, objects, and actions in the world around us is a crucial aspect of
human perception that allows us to plan and act in our environment. Remarkably, our …

Multi-view action recognition aims to identify action categories from given clues. Existing
studies ignore the negative influences of fuzzy views between view and action in …

Many behaviors rely on predictions derived from recent visual input, but the temporal
evolution of those inputs is generally complex and difficult to extrapolate. We propose that …

Standard methods for video recognition use large CNNs designed to capture spatio-
temporal data. However, training these models requires a large amount of labeled training …

Humans can effortlessly recognize others' actions in the presence of complex
transformations, such as changes in viewpoint. Several studies have located the regions in …

Humans can recognize and communicate about many actions performed by others. How are
actions organized in the mind, and is this organization shared across vision and language …