Results 11 to 20 of about 1,314,055 (250)
Architecturing Binarized Neural Networks for Traffic Sign Recognition [PDF]
International Conference on Artificial Neural Networks, 2023 Traffic signs support road safety and managing the flow of traffic, hence are an integral part of any vision system for autonomous driving. While the use of deep learning is well-known in traffic signs classification due to the high accuracy results ...
Andreea Postovan, Madalina Erascu
semanticscholar +3 more sources
An adiabatic method to train binarized artificial neural networks [PDF]
Scientific Reports, 2021 An artificial neural network consists of neurons and synapses. Neuron gives output based on its input according to non-linear activation functions such as the Sigmoid, Hyperbolic Tangent (Tanh), or Rectified Linear Unit (ReLU) functions, etc..
Yuansheng Zhao, Jiang Xiao
doaj +4 more sources
MOBIUS: Model-Oblivious Binarized Neural Networks [PDF]
IEEE Access, 2019 A privacy-preserving framework in which a computational resource provider receives encrypted data from a client and returns prediction results without decrypting the data, i.e., oblivious neural network or encrypted prediction, has been studied in ...
Hiromasa Kitai +9 more
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A Review of Binarized Neural Networks [PDF]
Electronics, 2019 In this work, we review Binarized Neural Networks (BNNs). BNNs are deep neural networks that use binary values for activations and weights, instead of full precision values.
Taylor Simons, Dah-Jye Lee
semanticscholar +2 more sources
Sparsity-Inducing Binarized Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Binarization of feature representation is critical for Binarized Neural Networks (BNNs). Currently, sign function is the commonly used method for feature binarization.
Peisong Wang +4 more
semanticscholar +3 more sources
Portfolio solver for verifying Binarized Neural Networks [PDF]
Annales Mathematicae et Informaticae, 2021 Although deep learning is a very successful AI technology, many concerns have been raised about to what extent the decisions making process of deep neural networks can be trusted.
Gergely Kovásznai +2 more
semanticscholar +4 more sources
Verifying Properties of Binarized Deep Neural Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2018 Understanding properties of deep neural networks is an important challenge in deep learning. In this paper, we take a step in this direction by proposing a rigorous way of verifying properties of a popular class of neural networks, Binarized Neural ...
Kasiviswanathan, Shiva Prasad +4 more
core +3 more sources
Binarized graph neural network [PDF]
World Wide Web, 2021 Recently, there have been some breakthroughs in graph analysis by applying the graph neural networks (GNNs) following a neighborhood aggregation scheme, which demonstrate outstanding performance in many tasks. However, we observe that the parameters of the network and the embedding of nodes are represented in real-valued matrices in existing GNN-based ...
Hanchen Wang +6 more
openaire +2 more sources
Neural Spike Sorting Using Binarized Neural Networks [PDF]
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021 This article presents the design and efficient hardware implementation of binarized neural networks (BNNs) for brain-implantable neural spike sorting. In contrast to the conventional artificial neural networks (ANNs), in which the weights and activation functions of neurons are represented using real values, the BNNs utilize binarized weights and ...
Daniel Valencia, Amir Alimohammad
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A Mixed Integer Programming Approach for Verifying Properties of Binarized Neural Networks [PDF]
AISafety@IJCAI, 2022 Many approaches for verifying input-output properties of neural networks have been proposed recently. However, existing algorithms do not scale well to large networks. Recent work in the field of model compression studied binarized neural networks (BNNs),
Christopher Lazarus +1 more
semanticscholar +1 more source