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Channel Equilibrium Networks for Learning Deep Representation
[article]
2020
arXiv
pre-print
Preserved Fulltext
channels at the same layer to contribute equally to the learned representation. ...
However, this work shows that the combination of normalization and rectified linear function leads to inhibited channels, which have small magnitude and contribute little to the learned feature representation ...
Channel Equilibrium Networks for Learning Deep Representation
Input
Residual
Conv (1x1),
BN, ReLU
Conv (3x3),
BN, ReLU
Conv (1x1),
BN
CE
ReLU
+
(c) CE residual block in ResNet
Input ...
arXiv:2003.00214v1
fatcat:tfrfhunicffl5bvg4ur35knkzm
Stress Field Prediction in Cantilevered Structures Using Convolutional Neural Networks
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1808.08914v1 fatcat:f6aeom2kpjf55gxo3vnmma2jtaOther Versions
The first is a single input channel network called SCSNet as the baseline architecture, and the second is the multi-channel input network called StressNet. ...
As one step toward accelerating structural analysis, this work explores a deep learning based approach for predicting the stress fields in 2D linear elastic cantilevered structures subjected to external ...
Introduction Deep learning is one of machine learning methods with multiple levels of representation, obtained by composing simple but non-linear modules that each transform the representation at one level ...
arXiv:1808.08914v2
fatcat:mx6oksl5n5eptkxmeqtahwtmam
Learning Paired-associate Images with An Unsupervised Deep Learning Architecture
[article]
2014
arXiv
pre-print
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The system uses a deep learning architecture (DLA) composed of two input/output channels formed from stacked Restricted Boltzmann Machines (RBM) and an associative memory network that combines the two ...
This paper presents an unsupervised multi-modal learning system that learns associative representation from two input modalities, or channels, such that input on one channel will correctly generate the ...
These two sensory channels can come together to allow paired-associate learning, but their individual channel representations can be learned and improved upon separately. ...
arXiv:1312.6171v2
fatcat:3loqpnxftrcsvmsvr4qesawioe
Bootstrap Equilibrium and Probabilistic Speaker Representation Learning for Self-supervised Speaker Verification
[article]
2021
arXiv
pre-print
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2021 accepted arXiv:2112.08929v2 fatcat:bfbcd26k7fcrteg25fhasniika
In this paper, we propose self-supervised speaker representation learning strategies, which comprise of a bootstrap equilibrium speaker representation learning in the front-end and an uncertainty-aware ...
Experimental results show that the proposed bootstrap equilibrium training strategy can effectively help learn the speaker representations and outperforms the conventional methods based on contrastive ...
The uncer-
learning the bootstrap equilibrium speaker representations. ...
arXiv:2112.08929v1
fatcat:cm4plnaw2ngtnk23s5pq3cmjhe
Deep Generative Adversarial Neural Networks for Realistic Prostate Lesion MRI Synthesis
[article]
2017
arXiv
pre-print
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Synthetic outputs are compared to real images and the implicit latent representations induced by the GAN are explored. ...
can be used in the medical domain to create realistic looking synthetic lesion images. 16mm x 16mm patches are extracted from 330 MRI scans from the SPIE ProstateX Challenge 2016 and used to train a Deep ...
Acknowledgments The authors would like to acknowledge the organizers of the SPIE ProstateX Challenge 2016 for their hard work in organizing the competition and preparing the training data used in this ...
arXiv:1708.00129v1
fatcat:ucburqtk4zem5avrnx7pjvuska
Chinese Sentence Semantic Matching Based on Multi-Granularity Fusion Model
[chapter]
2020
Lecture Notes in Computer Science
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In addition, we propose the equilibrium cross-entropy, a novel loss function, by setting mean square error (MSE) as an equilibrium factor of cross-entropy. ...
The experimental results conducted on Chinese open data set demonstrate that our proposed model combined with binary equilibrium cross-entropy loss function is superior to the existing state-of-the-art ...
DFF is a deep feature fusion model for sentence representation, which is integrated into the popular deep architecture for SSM task [25] . ...
doi:10.1007/978-3-030-47436-2_19
fatcat:ziczkmpowzhvjleezefh6vnw5e
Training Graph Neural Networks with 1000 Layers
[article]
2022
arXiv
pre-print
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Our models RevGNN-Deep (1001 layers with 80 channels each) and RevGNN-Wide (448 layers with 224 channels each) were both trained on a single commodity GPU and achieve an ROC-AUC of 87.74 ± 0.13 and 88.24 ...
Deep graph neural networks (GNNs) have achieved excellent results on various tasks on increasingly large graph datasets with millions of nodes and edges. ...
Acknowledgments The authors thank Shaojie Bai, researchers at Intel ISL, the reviewers, and area chairs for their helpful suggestions. ...
arXiv:2106.07476v3
fatcat:fa5bkhyy6fhmpkap2koir4jbz4
Multiscale Deep Equilibrium Models
[article]
2020
arXiv
pre-print
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We propose a new class of implicit networks, the multiscale deep equilibrium model (MDEQ), suited to large-scale and highly hierarchical pattern recognition domains. ...
An MDEQ directly solves for and backpropagates through the equilibrium points of multiple feature resolutions simultaneously, using implicit differentiation to avoid storing intermediate states (and thus ...
Is implicit deep learning relevant for general pattern recognition tasks? One clear challenge here is that implicit networks do away with flexible "layers" and "stages". ...
arXiv:2006.08656v2
fatcat:6kogo3mlubbajdoqfz3fbxwyi4
Deep Learning Convective Flow Using Conditional Generative Adversarial Networks
[article]
2020
arXiv
pre-print
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We developed a general deep learning framework, FluidGAN, that is capable of learning and predicting time-dependent convective flow coupled with energy transport. ...
FluidGAN also learns the coupling between velocity, pressure and temperature fields. ...
The authors would like to thank Zhonglin Cao, Nina Prakash, and Pranshu Pant for valuable comments and edits. ...
arXiv:2005.06422v1
fatcat:qpomg4kyvjdlrofov5ptzhd3iu
Analysis by Adversarial Synthesis — A Novel Approach for Speech Vocoding
2019
Interspeech 2019
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Classical parametric speech coding techniques provide a compact representation for speech signals. ...
In this work, we introduce a new methodology for neural speech vocoding based on generative adversarial networks (GANs). ...
When the training reaches an equilibrium state, the discriminator becomes fooled by the fake data created by the generator network, which is the target deep generative model. ...
doi:10.21437/interspeech.2019-1195
dblp:conf/interspeech/MustafaBBSM19
fatcat:4yeskn5mwbeijkjnzmkd34m7ji
PixelGame: Infrared small target segmentation as a Nash equilibrium
[article]
2022
arXiv
pre-print
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To address this problem, we propose a competitive game framework (pixelGame) from a novel perspective for ISTS. ...
We consider the Nash equilibrium of pixelGame as the optimal solution. In addition, we propose maximum information modulation (MIM) to highlight the tar-get information. ...
Different from traditional methods, deep convolutional neural networks (CNN) learn infrared small target representations in a data-driven manner. ...
arXiv:2205.13124v1
fatcat:6ykpdwyzqngq5k5jirxb563dw4
Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction
[article]
2021
arXiv
pre-print
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This autoencoder not only decreases the remote monitoring payload size by reducing the state representation dimension, but also learns the system dynamics by lifting it via a Koopman operator, thereby ...
with the representation dimension and transmission power. ...
Brunton, “Deep learning for universal
size. ...
arXiv:2104.08109v1
fatcat:ucla5onm4nedfgfiikvov55zne
Improving brain computer interface performance by data augmentation with conditional Deep Convolutional Generative Adversarial Networks
[article]
2018
arXiv
pre-print
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Inspired by generative adversarial networks, we propose a conditional Deep Convolutional Generative Adversarial (cDCGAN) Networks method to generate more artificial EEG signal automatically for data augmentation ...
The results show that the generated artificial EEG data from Gaussian noise can learn the features from raw EEG data and has no less than the classification accuracy of raw EEG data in the testing dataset ...
For our methods, we generate artificial EEG signal from probability distribution and deep learning perspective. ...
arXiv:1806.07108v2
fatcat:oewpyzl4lzbaharydnz5subx4m
Sentiment Analysis of Online Course Evaluation Based on a New Ensemble Deep Learning Mode: Evidence from Chinese
2021
Applied Sciences
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The model takes full advantage of Word2Vec and Glove in word vector representation, and utilizes the bidirectional long and short time network and convolutional neural network to achieve deep feature extraction ...
In recent years, online course learning has gradually become the mainstream of learning. ...
Acknowledgments: This study is fully supported by Science and education Joint project of Hunan Natural Science Foundation (2020JJ7031) and Fundamental Research Funds for the Central Universities (2019RC057 ...
doi:10.3390/app112311313
fatcat:zr3upkhegbgjjlqfh3c4rsg4mi
A Simple Recurrent Unit Model based Intrusion Detection System with DCGAN
2019
IEEE Access
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INDEX TERMS Network security, deep learning, intrusion detection system (IDS), simple recurrent unit, deep convolutional generative adversarial networks. 83286 2169-3536 ...
To address the challenge of unbalanced positive and negative learning samples, we propose using deep convolutional generative adversarial networks (DCGAN), which allows features to be extracted directly ...
data input for the proposed deep learning model. ...
doi:10.1109/access.2019.2922692
fatcat:qzea74ipcfalrkwht4feypkexi
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