In recent years, deep neural networks (DNNs) have received increased attention, have been
applied to different applications, and achieved dramatic accuracy improvements in many …

Deep neural networks have proven to be particularly effective in visual and audio
recognition tasks. Existing models tend to be computationally expensive and memory …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …

Deep neural networks (DNNs) have recently achieved great success in many visual
recognition tasks. However, existing deep neural network models are computationally …

The capacity of a neural network to absorb information is limited by its number of
parameters. Conditional computation, where parts of the network are active on a per …

In this paper, we propose a Runtime Neural Pruning (RNP) framework which prunes the
deep neural network dynamically at the runtime. Unlike existing neural pruning methods …

Making deep convolutional neural networks more accurate typically comes at the cost of
increased computational and memory resources. In this paper, we reduce this cost by …

Multi-task learning aims to improve generalization performance of multiple prediction tasks
by appropriately sharing relevant information across them. In the context of deep neural …

This paper proposes a deep learning architecture based on Residual Network that
dynamically adjusts the number of executed layers for the regions of the image. This …