Results 11 to 20 of about 203,239 (310)
Canonical convolutional neural networks
2022 International Joint Conference on Neural Networks (IJCNN), 2022 We introduce canonical weight normalization for convolutional neural networks. Inspired by the canonical tensor decomposition, we express the weight tensors in so-called canonical networks as scaled sums of outer vector products. In particular, we train network weights in the decomposed form, where scale weights are optimized separately for each mode ...
Lokesh Veeramacheneni +3 more
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Convolutional Neural Networks: A Survey
Computers, 2023 Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including ...
Moez Krichen
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Powerset Convolutional Neural Networks
CoRR, 2019 We present a novel class of convolutional neural networks (CNNs) for set functions, i.e., data indexed with the powerset of a finite set. The convolutions are derived as linear, shift-equivariant functions for various notions of shifts on set functions.
Wendler, Chris +2 more
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Differential convolutional neural network
Neural Networks, 2019 Convolutional neural networks with strong representation ability of deep structures have ever increasing popularity in many research areas. The main difference of Convolutional Neural Networks with respect to existing similar artificial neural networks is the inclusion of the convolutional part.
Mehmet Sarigul +2 more
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Convolutional Neural Networks With Dynamic Regularization [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Regularization is commonly used for alleviating overfitting in machine learning. For convolutional neural networks (CNNs), regularization methods, such as DropBlock and Shake-Shake, have illustrated the improvement in the generalization performance. However, these methods lack a self-adaptive ability throughout training.
Yi Wang 0068 +3 more
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Contextual Convolutional Neural Networks [PDF]
2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021 We propose contextual convolution (CoConv) for visual recognition. CoConv is a direct replacement of the standard convolution, which is the core component of convolutional neural networks. CoConv is implicitly equipped with the capability of incorporating contextual information while maintaining a similar number of parameters and computational cost ...
Ionut Cosmin Duta +2 more
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Simplicial Convolutional Neural Networks
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Graphs can model networked data by representing them as nodes and their pairwise relationships as edges. Recently, signal processing and neural networks have been extended to process and learn from data on graphs, with achievements in tasks like graph signal reconstruction, graph or node classifications, and link prediction.
Maosheng Yang, Elvin Isufi, Geert Leus
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Orthogonal Convolutional Neural Networks [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 Deep convolutional neural networks are hindered by training instability and feature redundancy towards further performance improvement. A promising solution is to impose orthogonality on convolutional filters. We develop an efficient approach to impose filter orthogonality on a convolutional layer based on the doubly block-Toeplitz matrix ...
Jiayun Wang +3 more
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Voronoi Convolutional Neural Networks
CoRR, 2020 Technical ...
Soroosh Yazdani, Andrea Tagliasacchi
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Кібернетика та комп'ютерні технології, 2021 Introduction. The implementation of information technologies in various spheres of public life dictates the creation of efficient and productive systems for entering information into computer systems. In such systems it is important to build an effective
Elshan Mustafayev, Rustam Azimov
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