Results 11 to 20 of about 298,507 (214)

Deep oscillatory neural network

Scientific Reports
We propose the Deep Oscillatory Neural Network (DONN), a brain-inspired network architecture that incorporates oscillatory dynamics into learning.
Nurani Rajagopal Rohan, C. Vigneswaran, Sayan Ghosh, Kishore Rajendran, A. Gaurav, V. Srinivasa Chakravarthy   +5 more
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Evolutional deep neural network [PDF]

Physical Review E, 2021
The notion of an Evolutional Deep Neural Network (EDNN) is introduced for the solution of partial differential equations (PDE). The parameters of the network are trained to represent the initial state of the system only, and are subsequently updated dynamically, without any further training, to provide an accurate prediction of the evolution of the PDE
Yifan Du, Tamer A. Zaki
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Orthogonal Deep Neural Networks [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
In this paper, we introduce the algorithms of Orthogonal Deep Neural Networks (OrthDNNs) to connect with recent interest of spectrally regularized deep learning methods. OrthDNNs are theoretically motivated by generalization analysis of modern DNNs, with the aim to find solution properties of network weights that guarantee better generalization.
Shuai Li, Kui Jia, Yuxin Wen, Tongliang Liu, Dacheng Tao   +4 more
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Tweaking Deep Neural Networks [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Deep neural networks are trained so as to achieve a kind of the maximum overall accuracy through a learning process using given training data. Therefore, it is difficult to fix them to improve the accuracies of specific problematic classes or classes of interest that may be valuable to some users or applications.
Kim, Jinwook, Yoon, Heeyong, Kim, Min-Soo   +2 more
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Deep Polynomial Neural Networks [PDF]

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021
Deep Convolutional Neural Networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention ...
Chrysos, G.G., Moschoglou, S., Bouritsas, G., Deng, J., Panagakis, Y., Zafeiriou, S.   +5 more
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Deep Neural Networks

, 2022
AbstractThere are many articles teaching people how to build intelligent applications using different frameworks such as TensorFlow, PyTorch, etc. However, except those very professional research papers, very few articles can give us a comprehensive understanding on how to develop such frameworks.
Liang Wang, Jianxin Zhao
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Deep Neural Networks for Network Routing [PDF]

2019 International Joint Conference on Neural Networks (IJCNN), 2019
In this work, we propose a Deep Learning (DL) based solution to the problem of routing traffic flows in computer networks. Routing decisions can be made in different ways depending on the desired objective and, based on that objective function, optimal solutions can be computed using a variety of techniques, e.g.
Reis, João, Rocha, Miguel, Phan, Truong Khoa, Griffin, David, Le, Franck, Rio, Miguel   +5 more
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Evolving Deep Neural Networks [PDF]

, 2019
The success of deep learning depends on finding an architecture to fit the task. As deep learning has scaled up to more challenging tasks, the architectures have become difficult to design by hand. This paper proposes an automated method, CoDeepNEAT, for optimizing deep learning architectures through evolution.
Miikkulainen, Risto, Liang, Jason, Meyerson, Elliot, Rawal, Aditya, Fink, Dan, Francon, Olivier, Raju, Bala, Shahrzad, Hormoz, Navruzyan, Arshak, Duffy, Nigel, Hodjat, Babak   +10 more
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The use of adversaries for optimal neural network training [PDF]

EPJ Web of Conferences, 2019
B-decay data from the Belle experiment at the KEKB collider have a substantial background from e+e- -h> qq¯ events. To suppress this we employ deep neural network algorithms. These provide improved signal from background discrimination. However, the deep
Hawthorne-Gonzalvez Anton, Sevior Martin
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Dual-Precision Deep Neural Network [PDF]

Proceedings of the 2020 3rd International Conference on Artificial Intelligence and Pattern Recognition, 2020
On-line Precision scalability of the deep neural networks(DNNs) is a critical feature to support accuracy and complexity trade-off during the DNN inference. In this paper, we propose dual-precision DNN that includes two different precision modes in a single model, thereby supporting an on-line precision switch without re-training.
Park, Jae Hyun, Choi, Ji Sub, Ko, Jong Hwan   +2 more
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