Convolutional neural networks (CNNs) have been applied to visual tasks since the late
1980s. However, despite a few scattered applications, they were dormant until the mid …

In the last few years, deep learning has led to very good performance on a variety of
problems, such as visual recognition, speech recognition and natural language processing …

We show that Transformer encoder architectures can be sped up, with limited accuracy
costs, by replacing the self-attention sublayers with simple linear transformations that" mix" …

Pre-trained language models such as BERT have proven to be highly effective for natural
language processing (NLP) tasks. However, the high demand for computing resources in …

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

Despite significant accuracy improvement in convolutional neural networks (CNN) based
object detectors, they often require prohibitive runtimes to process an image for real-time …

To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most
existing methods prune neurons by only considering the statistics of an individual layer or …

The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

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

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields,
ranging from speech processing, image classification to drug discovery. This is driven by the …