Results 271 to 280 of about 1,721,209 (309)
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2006
On capacity considerations it is clear that it is advantageous to use non-binary neural networks.
Donald Prados, Subhash Kak
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On capacity considerations it is clear that it is advantageous to use non-binary neural networks.
Donald Prados, Subhash Kak
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daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices
ACM Multimedia, 2019It is always well believed that Binary Neural Networks (BNNs) could drastically accelerate the inference efficiency by replacing the arithmetic operations in float-valued Deep Neural Networks (DNNs) with bit-wise operations.
Jianhao Zhang +4 more
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DeltaNet: Differential Binary Neural Network
2019 IEEE 30th International Conference on Application-specific Systems, Architectures and Processors (ASAP), 2019Energy-constrained neural network processing is in high demanded for various mobile applications. Binarized neural network (BNN) aggressively enhances the computational efficiency, and in contrast, it suffers from degradation of accuracy due to its extreme approximation. We propose a neural network model using a new activation function "Delta" based on
Yuka Oba +4 more
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BinaryDenseNet: Developing an Architecture for Binary Neural Networks
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs, but suffer from substantial accuracy degradation compared to their real-valued counterparts on large-scale datasets, e.g., ImageNet.
Joseph Bethge +3 more
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Dual Path Binary Neural Network
2019 International SoC Design Conference (ISOCC), 2019Binary neural networks can effectively reduce the number of required parameters but might decrease the classification accuracy. To solve the problem, we propose a dual-path binary neural network (DPBNN) in this paper. Experimental results show that our DPBNN can outperform other traditional binary neural network in CIFAR-10 and SVHN dataset.
Pei-Yin Chen +3 more
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Are analog neural networks better than binary neural networks?
Circuits, Systems, and Signal Processing, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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The geometrical learning of binary neural networks
IEEE Transactions on Neural Networks, 1995In this paper, the learning algorithm called expand-and-truncate learning (ETL) is proposed to train multilayer binary neural networks (BNN) with guaranteed convergence for any binary-to-binary mapping. The most significant contribution of this paper is the development of a learning algorithm for three-layer BNN which guarantees the convergence ...
J H, Kim, S K, Park
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Supervised neural networks with memristor binary synapses
International Journal of Circuit Theory and Applications, 2018SummaryMemristors are emerging devices that promise the efficient implementation of synapses in artificial neural networks. Memristors have permitted the processing and analysis of a large amount of data in evolutionary learning artificial systems through signals that can be assimilated to human brain‐like neurotransmitters and synapses.
Jacopo, Secco +2 more
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Fast binary cellular neural networks
Proceedings of International Conference on Neural Networks (ICNN'97), 2002We show that for a class of monotonic binary CNNs one can speed up the analog transient by introducing simple non-linear cell interconnections. Employing additional parameter optimization we acquire the speed gain of up to two orders of magnitude.
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