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Point Convolutional Neural Networks by Extension Operators [article]

Matan Atzmon, Haggai Maron, Yaron Lipman
2018 arXiv   pre-print
This paper presents Point Convolutional Neural Networks (PCNN): a novel framework for applying convolutional neural networks to point clouds.  ...  A point cloud convolution is defined by pull-back of the Euclidean volumetric convolution via an extension-restriction mechanism.  ...  Acknowledgements This research was supported in part by the European Research Council (ERC Consolidator Grant, "Lift-Match" 771136), the Israel Science Foundation (Grant No. 1830/17).  ... 
arXiv:1803.10091v1 fatcat:oq7wp3sytbb4hjpmup3dnv325q

Special Aspects of Matrix Operation Implementations for Low-Precision Neural Network Model on the Elbrus Platform

E.E. Limonova, Federal Research Center ", M.I. Neiman-zade, V.L. Arlazarov, Computer Science and Control", of the Russian Academy of Sciences, Smart Engines Service LLC, JSC "MCST", Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences
2020 Bulletin of the South Ural State University Series Mathematical Modelling Programming and Computer Software  
for performing fast floating point calculations and require the development of new approaches to increase the computational efficiency of neural network models.  ...  In this paper, we consider an 8-bit neural network model, in which matrix multiplication is the most resource-intensive part of the implementation.  ...  Let us inspect a convolutional layer of a neural network.  ... 
doi:10.14529/mmp200109 fatcat:7txfuvur35dovkvkuzsdmixxrm

Development of 3D Analysis Techniques

Kazuhiro Terao
2018 Zenodo  
"Classical" Neural Net 64 input feature map (b) Feed-forward neural network neuron Convolutional Neural Networks Toy visualization of the CNN operation 66 Convolutional Neural Networks  ...  Multi-Task Network Cascade • Chain of Segmentation + Detection -Feature points: "shower start" and "track edges" -Classify each pixel into "shower" vs. " track" • Extension to 3D data -Change in tensor  ...  By picking a value for w and b, we define a boundary between the two sets of data • CNNs are "feature extraction machine" -Consists of "convolution layers" with "kernels" -A chain of linear algebra operations  ... 
doi:10.5281/zenodo.2642420 fatcat:6zykef74yndqpelwvn5dsozdb4

NeurVPS: Neural Vanishing Point Scanning via Conic Convolution [article]

Yichao Zhou and Haozhi Qi and Jingwei Huang and Yi Ma
2021 arXiv   pre-print
We present a simple yet effective end-to-end trainable deep network with geometry-inspired convolutional operators for detecting vanishing points in images.  ...  In this work, we identify a canonical conic space in which the neural network can effectively compute the global geometric information of vanishing points locally, and we propose a novel operator named  ...  Based on the canonical space and the conic convolution operator, we are able to design the convolutional neural network that accurately predicts the vanishing points.  ... 
arXiv:1910.06316v3 fatcat:ttkxj6amh5g3rk7pr2ab6kkfvm

Neural architecture search as program transformation exploration

Jack Turner, Elliot J. Crowley, Michael F. P. O'Boyle
2021 Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems  
In contrast, NAS techniques mutate networks by operations such as the grouping or bottlenecking of convolutions, exploiting the resilience of DNNs.  ...  KEYWORDS program transformations, neural networks ACM Reference Format:  ...  CONCLUSIONS AND FUTURE WORK This paper presents a new unified program transformation approach to optimizing convolution neural networks by expressing neural model operations as formal program transformations  ... 
doi:10.1145/3445814.3446753 fatcat:mchoutirjja3nerrrkipt2vvxa

Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration [article]

Saad Abbasi, Mohammad Javad Shafiee, Ellick Chan, Alexander Wong
2021 arXiv   pre-print
Finally, we analyze the inference time reductions using hardware-specific acceleration when compared to native deep learning frameworks across a wide variety of hand-crafted deep convolutional neural network  ...  , with form often dictated by accuracy as opposed to hardware function.  ...  We find that in the case of increasing convolutional widths, the network execution time is correlated with the number of floating point operations for each design pattern.  ... 
arXiv:2107.04144v1 fatcat:pqluydcz5rhpxm6pbu527nvaji

A Fringe Phase Extraction Method Based on Neural Network

Wenxin Hu, Hong Miao, Keyu Yan, Yu Fu
2021 Sensors  
This paper proposes an end-to-end method of fringe phase extraction based on the neural network.  ...  This method uses the U-net neural network to directly learn the correspondence between the gray level of a fringe pattern and the wrapped phase map, which is simpler than the exist deep learning methods  ...  Due to modulation by the object's height, light that was supposed to obtain at point B was cast on point E, but the light point recorded by the camera was A.  ... 
doi:10.3390/s21051664 pmid:33670957 pmcid:PMC7957713 fatcat:npptgoqdvrhs5cfog5qv5njbh4

All You Need Is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification

Weijie Chen, Di Xie, Yuan Zhang, Shiliang Pu
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
It surpasses other counterparts constructed by depthwise separable convolution and the networks searched by NAS in terms of accuracy and practical speed.  ...  To put this direction forward, a new and novel basic component named Sparse Shift Layer (SSL) is introduced in this paper to construct efficient convolutional neural networks.  ...  A compact network can be constructed by interleaving this operation with point-wise convolutions.  ... 
doi:10.1109/cvpr.2019.00741 dblp:conf/cvpr/ChenXZP19 fatcat:fwvpjqo63za7doqan2hdmyy5qa

All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification [article]

Weijie Chen and Di Xie and Yuan Zhang and Shiliang Pu
2019 arXiv   pre-print
It surpasses other counterparts constructed by depthwise separable convolution and the networks searched by NAS in terms of accuracy and practical speed.  ...  To put this direction forward, a new and novel basic component named Sparse Shift Layer (SSL) is introduced in this paper to construct efficient convolutional neural networks.  ...  A compact network can be constructed by interleaving this operation with point-wise convolutions.  ... 
arXiv:1903.05285v1 fatcat:j4jm427kobbrxbfvpahjnz47mu

GGM-Net:Graph Geometric Moments Convolution Neural Network for Point Cloud Shape Classification

Dilong Li, Xin Shen, Yongtao Yu, Haiyan Guan, Hanyun Wang, Deren Li
2020 IEEE Access  
by using an addition operation.  ...  To address this issue, we propose a graph geometric moments convolution neural network (GGM-Net), which learns local geometric features from geometric moments representation of a local point set.  ...  convolutional neural network.  ... 
doi:10.1109/access.2020.3007630 fatcat:yagx5d5wancapb7e4m5piysnbi

Extensible Embedded Processor for Convolutional Neural Networks

Joshua Misko, Shrikant S. Jadhav, Youngsoo Kim
2021 Scientific Programming  
Convolutional neural networks (CNNs) require significant computing power during inference.  ...  Methods for reducing memory size and increasing execution speed have been explored, but choosing effective techniques for an application requires extensive knowledge of the network architecture.  ...  In Soo Ahn for constructive criticism of the earlier version of the manuscript. is work was supported in part by the Bradley University Provost Office through 2019-2021 Caterpillar Award and Teaching Excellence  ... 
doi:10.1155/2021/6630552 doaj:55bc1abbad384d13b223988f1c0210fd fatcat:7tyz34ottvavpahkc5rntrbgzq

Arrhythmia Classifier using Binarized Convolutional Neural Network for Resource-Constrained Devices [article]

Ao Wang, Wenxing Xu, Hanshi Sun, Ninghao Pu, Zijin Liu, Hao Liu
2022 arXiv   pre-print
In recent years, deep learning and convolutional neural networks have been widely used in the classification of cardiac arrhythmias.  ...  In this paper, a binarized convolutional neural network suitable for ECG monitoring is proposed, which is hardware-friendly and more suitable for use in resource-constrained wearable devices.  ...  Methods of Binarization The convolutional layers of a convolutional neural network contain lots of convolutional computations, and these operations are all floating-point multiplications and additions.  ... 
arXiv:2205.03661v2 fatcat:hlefq4dnazhjnblrwoly6twy3m

Going Deeper in Frequency Convolutional Neural Network: A Theoretical Perspective [article]

Xiaohan Zhu, Zhen Cui, Tong Zhang, Yong Li, Jian Yang
2021 arXiv   pre-print
Convolutional neural network (CNN) is one of the most widely-used successful architectures in the era of deep learning.  ...  In this work, we revisit the Fourier transform theory to derive feed-forward and back-propagation frequency operations of typical network modules such as convolution, activation and pooling.  ...  Our work will promote the work of frequency domain convolutional neural network, as the starting point of relevant research.  ... 
arXiv:2108.05690v1 fatcat:ebdsb7ibbrg7tk75pa6awq2pnq

A Standardized PMML Format for Representing Convolutional Neural Networks with Application to Defect Detection

Max Ferguson, Yung-Tsun Tina Lee, Anantha Narayanan, Kincho H Law
2019 Smart and Sustainable Manufacturing Systems  
This paper seeks to address this issue by proposing a standardized format for convolutional neural networks, based on the Predictive Model Markup Language (PMML).  ...  Convolutional neural networks are becoming a popular tool for image processing in the engineering and manufacturing sectors.  ...  Such identification does not imply recommendation or endorsement by NIST; nor does it imply that the products identified are necessarily the best available for the purpose.  ... 
pmid:33029582 pmcid:PMC7537490 fatcat:2j5iq5zyg5a37pk7ycivmbobji

Generalized Dilation Neural Networks [article]

Gavneet Singh Chadha, Jan Niclas Reimann, Andreas Schwung
2019 arXiv   pre-print
Vanilla convolutional neural networks are known to provide superior performance not only in image recognition tasks but also in natural language processing and time series analysis.  ...  Second we break up the strict dilation structure, in that we develop kernels operating independently in the input space.  ...  Conclusion We presented generalized dilation neural networks, a NN architecture based on CNNs augmented with dilated filters. We provided extensions to this framework in two ways.  ... 
arXiv:1905.02961v1 fatcat:ctivy5g3jjhnjafy5k7chbvv7u
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