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deep neural networks (võtmesõna)
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1
artikkel kogumikus
Challenges in using neural networks in safety-critical applications
Forsberg, H.
;
Linden, Jan
;
Hjorth, J.
;
Manefjord, T.
;
Daneshtalab, Masoud
AIAA/IEEE Digital Avionics Systems Conference - Proceedings,
2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), Virtual Conference, October 11-16, 2020 : Proceedings
2020
/
7 p
https://doi.org/10.1109/DASC50938.2020.9256519
artikkel kogumikus
2
artikkel kogumikus
Composing graph theory and deep neural networks to evaluate SEU type soft error effects
Balakrishnan, Aneesh
;
Lange, Thomas
;
Glorieux, Maximilien
;
Alexandrescu, Dan
;
Jenihhin, Maksim
9th Mediterranean Conference on Embedded Computing (MECO'2020), Budva, Montenegro, 8-11 June 2020
2020
https://doi.org/10.1109/MECO49872.2020.9134279
artikkel kogumikus
Seotud publikatsioonid
1
A synthetic, hierarchical approach for modelling and managing complex systems' quality and reliability = Sünteetiline, hierarhiline lähenemine keerukate süsteemide kvaliteedi ja töökindluse modelleerimiseks ja haldamiseks
3
artikkel kogumikus EST
/
artikkel kogumikus ENG
Data augmentation and teacher-student training for LF-MMI based robust speech recognition
Ullah, Asad
;
Alumäe, Tanel
Text, Speech, and Dialogue : 21st International Conference, TSD 2018, Brno, Czech Republic, September 11-14, 2018 : proceedings
2018
/
p. 403-410
https://doi.org/10.1007/978-3-030-00794-2_43
Conference Proceedings at Scopus
Article at Scopus
Article at WOS
artikkel kogumikus EST
/
artikkel kogumikus ENG
4
artikkel ajakirjas
DeepAxe : a framework for exploration of approximation and reliability trade-offs in DNN accelerators
Taheri, Mahdi
;
Riazati, Mohamad
;
Ahmadilivani, Mohammad Hasan
;
Jenihhin, Maksim
;
Daneshtalab, Masoud
;
Raik, Jaan
;
Sjödin, Mikael
;
Lisper, Björn
arXiv.org
2023
/
8 p. : ill
https://doi.org/10.48550/arXiv.2303.0822
artikkel ajakirjas
5
artikkel kogumikus
DeepHLS: A complete toolchain for automatic synthesis of deep neural networks to FPGA
Riazati, Mohammad
;
Daneshtalab, Masoud
;
Sjodin, Mikael
;
Lisper, Bjorn
ICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, November 23-25, 2020, Virtual Conference : Proceedings
2020
/
4 p
https://doi.org/10.1109/ICECS49266.2020.9294881
artikkel kogumikus
6
artikkel kogumikus
Gate-level graph representation learning : a step towards the improved stuck-at faults analysis
Balakrishnan, Aneesh
;
Alexandrescu, Dan
;
Jenihhin, Maksim
;
Lange, Thomas
;
Glorieux, Maximilien
Proceedings of the Twenty Second International Symposium on Quality Electronic Design (ISQED) : Santa Clara, USA, 7-9 April 2021
2021
/
p. 24-30
https://doi.org/10.1109/ISQED51717.2021.9424256
artikkel kogumikus
Seotud publikatsioonid
1
A synthetic, hierarchical approach for modelling and managing complex systems' quality and reliability = Sünteetiline, hierarhiline lähenemine keerukate süsteemide kvaliteedi ja töökindluse modelleerimiseks ja haldamiseks
7
artikkel kogumikus EST
/
artikkel kogumikus ENG
ZED-TTE: Zone embedding and deep neural network based travel time estimation approach
Ounoughi, Chahinez
;
Yeferny, Taoufik
;
Ben Yahia, Sadok
2021 International Joint Conference on Neural Networks (IJCNN) : proceedings
2021
/
10 p
https://doi.org/10.1109/IJCNN52387.2021.9533456
Conference Proceedings at Scopus
Article at Scopus
Article at WOS
artikkel kogumikus EST
/
artikkel kogumikus ENG
Kirjeid leitud 7, kuvan
1 - 7
võtmesõna
163
1.
deep neural networks
2.
deep neural networks (DNNs)
3.
deep convolutional neural network
4.
deep neural network
5.
deep neural network compression
6.
artificial neural networks
7.
artificial neural networks (ANN)
8.
Constrained neural networks
9.
convolutional neural networks
10.
convolutional neural networks (CNNa)
11.
Convolutional Neural Networks (CNNs)
12.
Fuzzy neural networks
13.
neural networks
14.
pre-trained neural networks
15.
recurrent neural networks
16.
recurrent neural networks (RNN)
17.
deep donor-deep acceptor pairs
18.
deep brain stimulation
19.
Deep defects
20.
deep energy levels
21.
deep energy renovation
22.
deep eutectic solvents
23.
deep fake
24.
deep habits
25.
deep heat well
26.
deep inference
27.
deep layer
28.
deep learning
29.
deep learning (DL)
30.
deep learning classifier
31.
deep learning model
32.
deep learning network
33.
deep level
34.
deep level traps
35.
deep levels
36.
deep offshore
37.
deep packet inspection
38.
deep reinforcement learning
39.
Deep reinforcing learning SDN
40.
deep renovation
41.
deep understanding
42.
Deep-learning
43.
deep-learning (DL)
44.
embedded deep learning
45.
natural deep eutectic solvents
46.
nZEB deep energy renovation
47.
nZEB deep renovation
48.
artificial neural network
49.
Artificial neural network (ANN)
50.
Binarized Neural Network (BNN)
51.
cascaded forward neural network (CFNN)
52.
Convolutional Neural Network
53.
convolutional neural network (CNN)
54.
feedforward neural network (FFNN)
55.
Graph Convolutional Neural Network (GCN)
56.
GraphSAGE (Graph Based Neural Network)
57.
human neural progenitor cells
58.
neural activity
59.
neural architecture search
60.
neural controller
61.
neural development
62.
neural fuzzy modelling control
63.
neural network
64.
neural network architecture search
65.
neural network controller
66.
neural network predictive controller
67.
neural oscillations
68.
neural processing unit
69.
neural stimulation
70.
Radial Basis Function Neural Network (RBFNN)
71.
recurrent neural network language model
72.
spiking neural network (SNN)
73.
Ternary Neural Network
74.
adaptive networks
75.
ad-hoc sensor networks security
76.
automotive communication networks
77.
Bayesian Belief Networks
78.
Bayesian networks
79.
binary search networks
80.
binary sensor networks
81.
body area networks
82.
body sensor networks
83.
cellular networks
84.
communication networks
85.
complex networks
86.
computer networks
87.
content distribution networks
88.
content-addressable storage networks
89.
convolutional networks
90.
cross-border communication networks
91.
developmental networks
92.
diffusion networks
93.
distribution networks
94.
district heating networks
95.
Dynamic networks
96.
ecological networks
97.
economic networks
98.
electric power transmission networks
99.
electrical networks
100.
embedded networks/NoC
101.
gateways (computer networks)
102.
generative adversarial networks
103.
geodetic networks
104.
global production networks
105.
heating networks
106.
hypar-formed networks
107.
Impedance-source networks
108.
innovation networks
109.
IP networks
110.
iterative networks
111.
knowledge networks
112.
location-based social networks
113.
logistics networks
114.
low voltage networks
115.
low-voltage networks
116.
lumped parameter networks
117.
marketing networks
118.
metabolic networks and pathways
119.
mobile actuator-sensor networks (MAS-Net)
120.
NB-IoT networks
121.
networks
122.
networks coding
123.
neuro-fuzzy networks and guided missile
124.
non-terrestrial networks
125.
observational networks
126.
on-body networks (BANs)
127.
Online social networks
128.
Optical fiber networks
129.
optical fibre networks
130.
optical networks
131.
optimized routing approach for critical and emergency networks
132.
optimized routing approach for critical and emergency networks simulations
133.
packet switched networks
134.
partner networks
135.
phantom cellular networks
136.
private networks
137.
production networks
138.
Public safety networks
139.
public safety networks (PSN)
140.
public safety networks (PSNs)
141.
reconfigurable scan networks
142.
regional innovation networks
143.
scale-free networks
144.
sensor networks
145.
small district heating networks
146.
SME networks
147.
social networks
148.
social networks
149.
sorting networks
150.
supply networks
151.
transmission networks
152.
WBANs (wireless body area networks)
153.
wearable wireless networks
154.
wearable wireless networks (WWNs)
155.
weighted networks
156.
wireless ad hoc sensor networks
157.
Wireless Body Area Networks
158.
wireless body area networks (WBANs)
159.
wireless body-to-body networks (BBN)
160.
wireless sensor networks
161.
virtual networks
162.
5G networks
163.
5G networks
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