Add to ORKGHow to train a binary neural network (BinaryNet) with both high compression rate and high accuracy on large scale dataset? We answer this question through a careful analysis of previous work on BinaryNets, in terms of training strategies, regularization, and activation approximation. Our findings first reveal that a low learning rate is highly preferred to avoid frequent sign changes of the weights, which often makes the learning of BinaryNets unstable. Secondly, we propose to use PReLU instead of ReLU in a BinaryNet to conveniently absorb the scale factor for weights to the activation function, which enjoys high computation efficiency for binarized layers while maintains high approximation accuracy. Thirdly, we reveal that instead of impos...
Compact architectures, ternary weights and binary activations are two methods suitable for making ne...
The ever-growing computational demands of increasingly complex machine learning models frequently ne...
Efficient inference of Deep Neural Networks (DNNs) is essential to making AI ubiquitous. Two importa...
Thesis (Ph.D.)--University of Washington, 2020The recent renaissance of deep neural networks has lea...
Network binarization (i.e., binary neural networks, BNNs) can efficiently compress deep neural netwo...
We present a method to train self-binarizing neural networks, that is, networks that evolve their we...
Binary Neural Networks (BNNs) are compact and efficient by using binary weights instead of real-valu...
Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer qua...
Binary neural networks (BNNs) are an extremely promising method for reducing deep neural networks’ c...
Neural network binarization accelerates deep models by quantizing their weights and activations into...
Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of...
We study the use of binary activated neural networks as interpretable and explainable predictors in ...
Based on the assumption that there exists a neu-ral network that efficiently represents a set of Boo...
One-bit quantization is a general tool to execute a complex model,such as deep neural networks, on a...
The increase in sophistication of neural network models in recent years has exponentially expanded m...
Compact architectures, ternary weights and binary activations are two methods suitable for making ne...
The ever-growing computational demands of increasingly complex machine learning models frequently ne...
Efficient inference of Deep Neural Networks (DNNs) is essential to making AI ubiquitous. Two importa...
Thesis (Ph.D.)--University of Washington, 2020The recent renaissance of deep neural networks has lea...
Network binarization (i.e., binary neural networks, BNNs) can efficiently compress deep neural netwo...
We present a method to train self-binarizing neural networks, that is, networks that evolve their we...
Binary Neural Networks (BNNs) are compact and efficient by using binary weights instead of real-valu...
Neural networks have demonstrably achieved state-of-the art accuracy using low-bitlength integer qua...
Binary neural networks (BNNs) are an extremely promising method for reducing deep neural networks’ c...
Neural network binarization accelerates deep models by quantizing their weights and activations into...
Binary neural networks (BNNs) have received ever-increasing popularity for their great capability of...
We study the use of binary activated neural networks as interpretable and explainable predictors in ...
Based on the assumption that there exists a neu-ral network that efficiently represents a set of Boo...
One-bit quantization is a general tool to execute a complex model,such as deep neural networks, on a...
The increase in sophistication of neural network models in recent years has exponentially expanded m...
Compact architectures, ternary weights and binary activations are two methods suitable for making ne...
The ever-growing computational demands of increasingly complex machine learning models frequently ne...
Efficient inference of Deep Neural Networks (DNNs) is essential to making AI ubiquitous. Two importa...