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Neural Network Branching for Neural Network Verification
[article]
2019
arXiv
pre-print
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Vertically, the model trained on small neural networks achieves similar performance on large neural networks. ...
Formal verification of neural networks is essential for their deployment in safety-critical areas. ...
An all zeros embedding network mimicking the neural network is initialised. ...
arXiv:1912.01329v1
fatcat:lqdbojhsyrbu7bafulvkwwqddq
On Hiding Neural Networks Inside Neural Networks
[article]
2021
arXiv
pre-print
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Our novel framework hides the existence of a secret neural network with arbitrary desired functionality within a carrier network. ...
Modern neural networks often contain significantly more parameters than the size of their training data. ...
Steganography for neural networks. ...
arXiv:2002.10078v3
fatcat:trktocwvabh4fcfblu5w6qsf2y
Explanations for Neural Networks by Neural Networks
2022
Applied Sciences
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Our idea is to generate explanations via another neural network (called the Interpretation Network, or I-Net), which maps network parameters to a symbolic representation of the network function. ...
In this paper, we introduce a real-time approach for generating a symbolic representation of the function learned by a neural network. ...
Explanations for Neural Networks by Neural Networks To overcome this problem, we propose to implement I as a neural network. This concept is visualized in Figure 1 . ...
doi:10.3390/app12030980
fatcat:xfits2ed3fbw7gagdaxt6sjmey
Porcupine Neural Networks: Approximating Neural Network Landscapes
2018
Neural Information Processing Systems
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In particular, for two-layer neural networks we introduce Porcupine Neural Networks (PNNs) whose weight vectors are constrained to lie over a finite set of lines. ...
Neural networks have been used prominently in several machine learning and statistics applications. ...
For two-layer neural networks, we provide an affirmative answer to this question by introducing a family of constrained neural networks which we refer to as Porcupine Neural Networks (PNNs) (Figure 1- ...
dblp:conf/nips/FeiziJZT18
fatcat:v4b32efcrzbzjavrvjz2tbh2ai
Neural Networks
[article]
1997
arXiv
pre-print
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We review the theory of neural networks, as it has emerged in the last ten years or so within the physics community, emphasizing questions of biological relevance over those of importance in mathematical ...
A neural network is after all a dynamical system. ...
in a neural networks, being determined by its synaptic organization. ...
arXiv:cond-mat/9705270v1
fatcat:a6o6tndg5fhffpyjxzfidpiyl4
Neural Networks
[chapter]
2017
Elements of Robotics
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Computerized models of neural networks are an active topic of research in artificial intelligence. ...
The control of simple Braitenberg vehicles is very similar to the control of a living organism by its biological neural network. ...
Further Reading Haykin [1] and Rojas [4] are comprehensive textbooks on neural networks. David Kriesel wrote an online tutorial [3] that can be freely downloaded. ...
doi:10.1007/978-3-319-62533-1_13
fatcat:4k453ooyhbh2jjdzeqzmyjjxia
Neural Networks
[chapter]
1996
Handbook of Neural Computation
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2 k 2 2 : We now model the functions h k (x) by a neural network with outputs y k (x; w) where w is the set of weight parameters governing the neural network mapping. ...
In this chapter we aim to show that the`black box' stigma of neural networks is largely unjusti ed, and that there is actually considerable insight available into the way in which neural networks operate ...
doi:10.1201/9781420050646.ptb6
fatcat:iqjwhadvlvdqzcrswvyn5a7xke
Neural Networks
1996
Journal of the Operational Research Society
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Neural Networks J. G. ...
Taylor explaining the various types of neural network and their biological progenitors. ...
doi:10.1057/jors.1996.70
fatcat:nmwit4d5brcipimtbngdqplb7e
Neural networks
1996
ACM Computing Surveys
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The links to statistics have proved important in practical applications of neural networks. ...
A broader view of a neural network architecture involves treating the network as a statistical processor, characterized by making particular probabilistic assumptions about data. ...
doi:10.1145/234313.234348
fatcat:ogdd64dnbbfqlez2h57lkryafy
Neural Networks
1996
Journal of the Operational Research Society
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Neural Networks J. G. ...
Taylor explaining the various types of neural network and their biological progenitors. ...
doi:10.2307/3010740
fatcat:yugrmfpj7zbmfov6x5qwrzb7qu
Neural Networks
2001
American Journal of Psychiatry
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By contrast, striosomes (including the entire ventral striatum) tend to receive inputs from neural structures affiliated with the limbic system, particularly the amygdala. ...
By contrast, striosomes (including the entire ventral striatum) tend to receive inputs from neural structures affiliated with the limbic system, particularly the amygdala. ...
doi:10.1176/appi.ajp.158.1.21
pmid:11136627
fatcat:fpgng7oqcng7pkjasywyzpiqmu
Neural Networks
[chapter]
2011
Encyclopedia of Machine Learning
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Using Neural Networks to generate existence proofs Neural network methods can be used to create existence proofs or as a \demonstration of concept". ...
Relevance of Neural Network techniques to the Neuroscientist There are three main areas where neural network techniques can be useful to the neuroscientist. The rst is for regression. ...
doi:10.1007/978-0-387-30164-8_586
fatcat:sr5rzs7lhbazrie2qgljh5bvuq
In-network Neural Networks
[article]
2018
arXiv
pre-print
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We present N2Net, a system that implements binary neural networks using commodity switching chips deployed in network switches and routers. ...
Our system shows that these devices can run simple neural network models, whose input is encoded in the network packets' header, at packet processing speeds (billions of packets per second). ...
In this paper, we explore the feasibility of using an artificial neural network (NN) model as classifier in a switching chip, as a complement to existing lookup tables. ...
arXiv:1801.05731v1
fatcat:zojoroijgfhyvop2tl7zhcq67m
Neural Network Inversion for Multilayer Quaternion Neural Networks
2016
Computer Technology and Application
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The neural network inversion method has been studied as one of the neural network-based solutions. ...
On the other hand, the extension of the neural network to a higher-dimensional domain, e.g., complex-value or quaternion, has been proposed, and a number of higher-dimensional neural network models have ...
Acknowledgment This work was partly supported by a Grant-in-Aid for Scientific Research (#26330284) from the Japan
Neural Network Inversion for Multilayer Quaternion Neural Networks 82 Society for the ...
doi:10.17265/1934-7332/2016.02.002
fatcat:w5mxebjjk5fi7igj4ngulaiumi
Cortex Neural Network: learning with Neural Network groups
[article]
2018
arXiv
pre-print
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The Cortex Neural Network is an upper architecture of neural networks which motivated from cerebral cortex in the brain to handle different tasks in the same learning system. ...
We view the architecture of Cortex in the brain as a missing part in the design of the current artificial neural network. In this paper, we purpose Cortex Neural Network (CrtxNN). ...
Conclusion In this paper, we performed the CrtxNN as a bionic approach motivated by cortex in the human brain to solve complex cognition tasks based on Artificial Neural Network. ...
arXiv:1804.03313v1
fatcat:c4d3zqygifbuxd5zlrcdoirhke
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