Results 321 to 330 of about 1,718,101 (355)

Restricted Convolutional Neural Networks

Neural Processing Letters, 2018
In this paper, a new type of convolutional neural network is proposed which is inspired by cellular automata research. This model is referred to as “restricted convolutional neural network” and its characteristic is that the feature maps are not fully connected, i.e.
Mehran Mirkhan, Mohammad Reza Meybodi
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Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition

IEEE International Conference on Computer Vision, 2017
Recognizing fine-grained categories (e.g., bird species) highly relies on discriminative part localization and part-based fine-grained feature learning.
Heliang Zheng, Jianlong Fu, Tao Mei, Jiebo Luo   +3 more
semanticscholar   +1 more source

In vitro convolutional neural networks

Nature Machine Intelligence, 2022
Neural networks can be implemented by using purified DNA molecules that interact in a test tube. Convolutional neural networks to classify high-dimensional data have now been realized in vitro, in one of the most complex demonstrations of molecular programming so far. ; © 2022 Springer Nature Limited. Published 11 July 2022.
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A-optimal convolutional neural network

Neural Computing and Applications, 2016
In this paper, we propose a novel data representation-classification model learning algorithm. The model is a convolutional neural network (CNN), and we learn its parameters to achieve A-optimality. The input multi-instance data are represented by a CNN model, and then classified by a linear classification model.
Zihong Yin, Dehui Kong, Guoxia Shao, Xinran Ning, Warren Jin 0001, Jingyan Wang   +5 more
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Sparse Convolutional Neural Networks

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity. In this work, we show how to reduce the redundancy in these parameters using a sparse decomposition. Maximum sparsity is obtained by exploiting both inter-channel
Liu, Baoyuan, Wang, Min, Foroosh, Hassan, Tappen, Marshall, Penksy, Marianna   +4 more
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A Survey on Convolution Neural Networks

2020 IEEE REGION 10 CONFERENCE (TENCON), 2020
Major tools to implement any Artificial Intelligence and Machine Learning systems are Symbolic AI and Artificial Neural Network (ANN) AI. ANN has made a dramatic improvement in the versatile area of Machine Learning (ML). ANN is a gathering of vast number of weighted interconnected artificial neurons, initially invented with the inspiration of ...
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Quadtree Convolutional Neural Networks

2018
This paper presents a Quadtree Convolutional Neural Network (QCNN) for efficiently learning from image datasets representing sparse data such as handwriting, pen strokes, freehand sketches, etc. Instead of storing the sparse sketches in regular dense tensors, our method decomposes and represents the image as a linear quadtree that is only refined in ...
Pradeep Kumar Jayaraman, Jianhan Mei, Jianfei Cai 0001, Jianmin Zheng   +3 more
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Understanding of a convolutional neural network

International Conference on Emerging Technologies, 2017
Saad Albawi, T. A. Mohammed, Saad Al-Zawi   +2 more
semanticscholar   +1 more source

VoxNet: A 3D Convolutional Neural Network for real-time object recognition

IEEE/RJS International Conference on Intelligent RObots and Systems, 2015
D. Maturana, S. Scherer
semanticscholar   +1 more source

Fully hardware-implemented memristor convolutional neural network

Nature, 2020
P. Yao, Huaqiang Wu, B. Gao, Jianshi Tang, Qingtian Zhang, Wenqiang Zhang, J. Yang, He Qian   +7 more
semanticscholar   +1 more source