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BinaryConnect: Training Deep Neural Networks with binary weights during propagations

• Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
• NIPS
• 2015

Deep Neural Networks (DNN) have achieved state-of-the-art results in a wide range of tasks, with the best results obtained with large training sets and large models. In the past, GPUs enabled these… (More)

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

• Matthieu Courbariaux, Itay Hubara, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
• 2016

We introduce a method to train Binarized Neural Networks (BNNs) neural networks with binary weights and activations at run-time and when computing the parameters’ gradient at traintime. We conduct… (More)

Binarized Neural Networks

• Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
• NIPS
• 2016

In this work we introduce a binarized deep neural network (BDNN) model. BDNNs are trained using a novel binarized back propagation algorithm (BBP), which uses binary weights and binary neurons during… (More)

Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

• Itay Hubara, Matthieu Courbariaux, Daniel Soudry, Ran El-Yaniv, Yoshua Bengio
• Journal of Machine Learning Research
• 2017

We introduce a method to train Quantized Neural Networks (QNNs) — neural networks with extremely low precision (e.g., 1-bit) weights and activations, at run-time. At traintime the quantized weights… (More)

BinaryNet: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

• Matthieu Courbariaux, Yoshua Bengio
• ArXiv
• 2016

We introduce BinaryNet, a method which trains DNNs with binary weights and activations when computing parameters’ gradient. We show that it is possible to train a Multi Layer Perceptron (MLP) on… (More)

Training deep neural networks with low precision multiplications

• Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
• 2014

Multipliers are the most space and power-hungry arithmetic operators of the digital implementation of deep neural networks. We train a set of state-of-the-art neural networks (Maxout networks) on… (More)

Low precision arithmetic for deep learning

• Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David
• ArXiv
• 2014

Neural Networks with Few Multiplications

• Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, Yoshua Bengio
• ICLR
• 2016

For most deep learning algorithms training is notoriously time consuming. Since most of the computation in training neural networks is typically spent on floating point multiplications, we… (More)

L Ow Precision Storage for Deep Learning

• Matthieu Courbariaux, Jean-Pierre David, Yoshua Bengio
• 2015

We train a set of state of the art neural networks, the Maxout networks (Goodfellow et al., 2013a), on three benchmark datasets: the MNIST, CIFAR10 and SVHN, with three distinct storing formats:… (More)

BNN+: Improved Binary Network Training

• Sajad Darabi, Mouloud Belbahri, Matthieu Courbariaux, Vahid Partovi Nia
• ArXiv
• 2018

The deployment of Deep neural networks (DNN) on edge devices has been difficult because they are resource hungry. Binary neural networks (BNN) help to alleviate the prohibitive resource requirements… (More)

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