One-bit quantization is a general tool to execute a complex model,such as deep neural networks, on a device with limited resources,such as cell phones. Naively compressing weights into one bityields an extensive accuracy loss. One-bit models, therefore, re-quire careful re-training. Here we introduce a class functions de-vised to be used as a regularizer for re-training one-bit models. Us-ing a regularization function, specifically devised for binary quanti-zation, avoids heuristic touch of the optimization scheme and savesconsiderable coding effort
At present, the quantification methods of neural network models are mainly divided into post-trainin...
Quantization of deep neural networks is extremely essential for efficient implementations. Low-preci...
The neural network quantization is highly desired procedure to perform before running neural network...
Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer qua...
While neural networks have been remarkably successful in a wide array of applications, implementing ...
In deep neural networks (DNNs), there are a huge number of weights and multiply-and-accumulate (MAC)...
Machine learning, and specifically Deep Neural Networks (DNNs) impact all parts of daily life. Altho...
How to train a binary neural network (BinaryNet) with both high compression rate and high accuracy o...
Machine learning, and specifically Deep Neural Networks (DNNs) impact all parts of daily life. Altho...
We propose methods to train convolutional neural networks (CNNs) with both binarized weights and act...
The increase in sophistication of neural network models in recent years has exponentially expanded m...
Quantization has shown stunning efficiency on deep neural network, especially for portable devices w...
Quantization of deep neural networks is a common way to optimize the networks for deployment on ener...
Quantizing weights and activations of deep neural networks is essential for deploying them in resour...
We investigate the compression of deep neural networks by quantizing their weights and activations i...
At present, the quantification methods of neural network models are mainly divided into post-trainin...
Quantization of deep neural networks is extremely essential for efficient implementations. Low-preci...
The neural network quantization is highly desired procedure to perform before running neural network...
Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer qua...
While neural networks have been remarkably successful in a wide array of applications, implementing ...
In deep neural networks (DNNs), there are a huge number of weights and multiply-and-accumulate (MAC)...
Machine learning, and specifically Deep Neural Networks (DNNs) impact all parts of daily life. Altho...
How to train a binary neural network (BinaryNet) with both high compression rate and high accuracy o...
Machine learning, and specifically Deep Neural Networks (DNNs) impact all parts of daily life. Altho...
We propose methods to train convolutional neural networks (CNNs) with both binarized weights and act...
The increase in sophistication of neural network models in recent years has exponentially expanded m...
Quantization has shown stunning efficiency on deep neural network, especially for portable devices w...
Quantization of deep neural networks is a common way to optimize the networks for deployment on ener...
Quantizing weights and activations of deep neural networks is essential for deploying them in resour...
We investigate the compression of deep neural networks by quantizing their weights and activations i...
At present, the quantification methods of neural network models are mainly divided into post-trainin...
Quantization of deep neural networks is extremely essential for efficient implementations. Low-preci...
The neural network quantization is highly desired procedure to perform before running neural network...