A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Soft Realization: a Bio-inspired Implementation Paradigm
[article]
2018
arXiv
pre-print
Preserved Fulltext
The proposed paradigm mitigates major weaknesses of hard realization by (1) alleviating incompatibilities with today's soft and bio-inspired algorithms such as artificial neural networks, fuzzy systems ...
Moreover, the incoming nanotechnologies face increasing reliability issues that prevent them from being efficiently exploited in hard realization of applications. ...
Acknowledgements We would like to thank profeesor Caro Lucas (RIP) for his helpful discussions and advice, as well as his kind and great support during establishmnet of the Soft realization idea foundations ...
arXiv:1812.08430v1
fatcat:voc6sqpsjfa5hn2tztw5vq3ef4
Dynamic Neural Networks: A Survey
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2102.04906v2 fatcat:rqjjjsavdrglbmlujtc6x4t7vqOther Versions
Dynamic neural network is an emerging research topic in deep learning. ...
adaptive inference along the temporal dimension for sequential data such as videos and texts. ...
For example, in hierarchical multi-scale recurrent neural network (HM-RNN) [162] , when the low-level (character-level) model detects that the input satisfies certain conditions, it will "flush" (reset ...
arXiv:2102.04906v4
fatcat:zelspxwv6nel7kv2yu6ynakyuu
Neutrosophy for physiological data compression: in particular by neural nets using deep learning
2020
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Deep learning neural networks have recently exceeded the compression rates of algorithmic techniques for text. ...
The pace of data production continues to grow, leading to increased needs for efficient storage and transmission. ...
Finally for the videos a temporal redundancy is also exploited, an image of a film varying only little from the previous one. ...
doi:10.5281/zenodo.3988027
fatcat:ju6xbcnt5ngdbefhlq3h2ojfga
Self-Compression in Bayesian Neural Networks
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
We show that Bayesian neural networks automatically discover redundancy in model parameters, thus enabling self-compression, which is linked to the propagation of uncertainty through the layers of the ...
Current methods compress the networks by reducing the precision of the parameters or by eliminating redundant ones. ...
We exploit the uncertainty information inherent in Bayesian neural networks and propose a method for self-compression of convolutional neural networks. ...
arXiv:2111.05950v1
fatcat:u3fl6pooonghrj2e6haoolibju
1D-FALCON: Accelerating Deep Convolutional Neural Network Inference by Co-optimization of Models and Underlying Arithmetic Implementation
[chapter]
2017
Lecture Notes in Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Deep convolutional neural networks (CNNs), which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks, at the expense of high computational ...
We also introduce a new class of fast 1-D convolutions for CNNs using the Toom-Cook algorithm. ...
[14] proposed a Sparse Convolutional Neural Networks (SCNN) model that exploits both inter-channel and intra-channel redundancy to maximize sparsity in a model. ...
doi:10.1007/978-3-319-68612-7_3
fatcat:p65rpuqlzreffca4rwtrvmaeeu
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this work, we propose a novel deep learning framework, called a nested sparse network, which exploits an n-in-1-type nested structure in a neural network. ...
While many recent works focus on reducing the redundancy by eliminating unneeded weight parameters, it is not possible to apply a single deep network for multiple devices with different resources. ...
In this work, we aim to exploit a nested structure in a deep neural architecture which realizes an n-in-1 versatile network to conduct multiple tasks within a single neural network (see Figure 1 ). ...
doi:10.1109/cvpr.2018.00904
dblp:conf/cvpr/KimAO18
fatcat:mrnzlehq2zcfvgwnab2gvwf6om
Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition
[article]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
We design a nine-layer CNN for HCCR consisting of 3,755 classes, and devise an algorithm that can reduce the networks computational cost by nine times and compress the network to 1/18 of the original size ...
Such networks intuitively appear to incur high computational cost, and require the storage of a large number of parameters, which renders them unfeasible for deployment in portable devices. ...
[12] exploited the cross-channel or filter redundancy to formulate a low-rank basis for filters, and proposed filter and data reconstruction techniques for optimization. Zhang et al. ...
arXiv:1702.07975v1
fatcat:paxuegfmg5azdd445xd7esstru
Efficient Deep Learning in Network Compression and Acceleration
[chapter]
2018
Digital Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
In this chapter, I will present a comprehensive survey of several advanced approaches for efficient deep learning in network compression and acceleration. ...
While deep learning delivers state-of-the-art accuracy on many artificial intelligence tasks, it comes at the cost of high computational complexity due to large parameters. ...
Network densifying Another direct category for obtaining network compression and acceleration is to design more efficient but low-cost network architecture. ...
doi:10.5772/intechopen.79562
fatcat:ya65wwhk5neppgxrut5phd42dy
Wavelet-based Enhanced Medical Image Super-Resolution
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
To solve this problem, we propose a wavelet-based mini-grid network medical image super-resolution (WMSR) method, which is similar to the three-layer hidden-layer-based super-resolution convolutional neural ...
In order to ensure the reproducibility of the image, a method of adding a sub-pixel layer is proposed to realize the hidden layer, and replacing the small mini-grid-network on the hidden layer is of considerable ...
Additionally, mini-grid-network is added for exploiting the speed of the network due to its good quick performance on the network [29] . ...
doi:10.1109/access.2020.2974278
fatcat:ovgfi6ygnjeerd4522v4qndezi
Towards Modality Transferable Visual Information Representation with Optimal Model Compression
[article]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Although numerous approaches were developed for improving the image and video coding performance by removing the redundancies within visual signals, much less work has been dedicated to the transformation ...
of the visual signals to another well-established modality for better representation capability. ...
neural network for better signal representation capability. ...
arXiv:2008.05642v1
fatcat:cam5jnjcorelfaiy2p3s6hbgki
Spiking Neural Networks Hardware Implementations and Challenges
2019
ACM Journal on Emerging Technologies in Computing Systems
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2020 pre-print arXiv:2005.01467v1 fatcat:i7mtmpuxfnbmzpy367z7ys4xrm
They are expected to improve the computational performance and efficiency of neural networks, but are best suited for hardware able to support their temporal dynamics. ...
In this survey, we present the state of the art of hardware implementations of spiking neural networks and the current trends in algorithm elaboration from model selection to training mechanisms. ...
enable very large-scale neural simulation at low cost, both in time and power. ...
doi:10.1145/3304103
fatcat:p3frra3osnhybj4hkor4y5cyqm
Approximated Prediction Strategy for Reducing Power Consumption of Convolutional Neural Network Processor
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
Convolutional neural network (CNN) is becoming popular because of its great ability for accurate image recognition. ...
The LCP exploits redundancy of operations in CNN and only executes essential convolutions by an approximated prediction technique. ...
Computational cost of App-Conv must be low to realize power reduction through LCP. ...
doi:10.1109/cvprw.2016.113
dblp:conf/cvpr/UjiieHS16
fatcat:lt4h4cx2jzfxdjpsqo3qcj26jq
On Moving Target Techniques for Network Defense Security
2021
International journal of recent technology and engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
The traditional technologies, tools and procedures of any network cannot be protected from attackers due to the unchanged services and configurations of the networks. ...
Adversarial neural networks are neural network entries that lead to false classification results. ...
A defender designs space is a group of deep neural networks that have been made for the same mission, but it not influenced by the aforesaid attack. ...
doi:10.35940/ijrte.e5111.019521
fatcat:stts5i22abfvzpcf3cawvjvpmy
Encoding Complete Body Models Enables Task Dependent Optimal Behavior
2007
Neural Networks (IJCNN), International Joint Conference on
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2008; you can also visit the original URL.
The file type is application/pdf.
This paper shows that our approach accounts for two forms of effective human behavior based on exploiting kinematic redundancy. ...
We propose a neural network architecture (SURE REACH) that acquires complete body models through unsupervised learning. ...
Acknowledgments The authors are grateful for the fruitful discussions with their colleagues in Würzburg. This work was supported by the European commission contract no. FP6-511931. ...
doi:10.1109/ijcnn.2007.4371203
dblp:conf/ijcnn/HerbortB07
fatcat:dsiflmathrgtbjabav6s77b2ve
Design and Realization of an Efficient Large-Area Event-Driven E-Skin
2020
Sensors
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Overall, this work develops a systematic approach towards realizing a flexible event-driven information handling system on standard computer systems for large-scale e-skin with detailed descriptions on ...
While our previous works focused on the implementation and the experimental validation of the approach, this work now provides the consolidated foundations for realizing, designing, and understanding large-area ...
Sebastian Stenner for their technical assistance, and Mr. J. Rogelio Guadarrama-Olvera for his support with the H1 robot. ...
doi:10.3390/s20071965
pmid:32244511
pmcid:PMC7180917
fatcat:acaii3peyzemlmsozcwxmx4pga
« Previous
Showing results 1 — 15 out of 10,153 results