Large neural networks are difficult to deploy on mobile devices because ...
User-level differential privacy (DP) provides certifiable privacy guaran...
In this paper, we propose a fully differentiable quantization method for...
Training deep neural networks (DNNs) for meaningful differential privacy...
Quantization is a widely used technique to compress and accelerate deep
...
Binary Neural Networks (BNNs) rely on a real-valued auxiliary variable W...
Convolutional neural networks are able to learn realistic image priors f...
Many successful learning targets such as dice loss and cross-entropy los...
The newly proposed panoptic segmentation task, which aims to encompass t...
Object detection has made impressive progress in recent years with the h...
Long-range dependencies modeling, widely used in capturing spatiotempora...
Deep convolutional neural networks (CNNs) have shown appealing performan...
Deep neural networks have evolved remarkably over the past few years and...
Deep Neural Networks have achieved remarkable progress during the past f...
In recent years, Deep Neural Networks (DNN) based methods have achieved
...