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deep neural networks (võtmesõna)

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Kirjeid leitud 12
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1

artikkel kogumikus EST / artikkel kogumikus ENG

AdAM: adaptive fault-tolerant approximate multiplier for edge DNN acceleratorsTaheri, Mahdi; Cherezova, Natalia; Nazari, Samira; Rafiq, Ahsan; Azarpeyvand, Ali; Ghasempouri, Tara; Daneshtalab, Masoud; Raik, Jaan; Jenihhin, Maksim2024 IEEE European Test Symposium (ETS): ETS 2024 : May 20-24, 2024, The Hague, Netherlands : proceedings2024 https://doi.org/10.1109/ETS61313.2024.10567161 Conference Proceedings at Scopus Article at Scopus Article at WOS

artikkel kogumikus EST / artikkel kogumikus ENG
2

artikkel kogumikus

Challenges in using neural networks in safety-critical applicationsForsberg, H.; Linden, Jan; Hjorth, J.; Manefjord, T.; Daneshtalab, MasoudAIAA/IEEE Digital Avionics Systems Conference - Proceedings, 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), Virtual Conference, October 11-16, 2020 : Proceedings2020 / 7 p https://doi.org/10.1109/DASC50938.2020.9256519

artikkel kogumikus
3

artikkel kogumikus

Composing graph theory and deep neural networks to evaluate SEU type soft error effectsBalakrishnan, Aneesh; Lange, Thomas; Glorieux, Maximilien; Alexandrescu, Dan; Jenihhin, Maksim9th Mediterranean Conference on Embedded Computing (MECO'2020), Budva, Montenegro, 8-11 June 20202020 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
4

artikkel kogumikus EST / artikkel kogumikus ENG

Data augmentation and teacher-student training for LF-MMI based robust speech recognitionUllah, Asad; Alumäe, TanelText, Speech, and Dialogue : 21st International Conference, TSD 2018, Brno, Czech Republic, September 11-14, 2018 : proceedings2018 / 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
5

artikkel ajakirjas

DeepAxe : a framework for exploration of approximation and reliability trade-offs in DNN acceleratorsTaheri, Mahdi; Riazati, Mohamad; Ahmadilivani, Mohammad Hasan; Jenihhin, Maksim; Daneshtalab, Masoud; Raik, Jaan; Sjödin, Mikael; Lisper, BjörnarXiv.org2023 / 8 p. : ill https://doi.org/10.48550/arXiv.2303.08226

artikkel ajakirjas
6

artikkel kogumikus

DeepHLS: A complete toolchain for automatic synthesis of deep neural networks to FPGARiazati, Mohammad; Daneshtalab, Masoud; Sjodin, Mikael; Lisper, BjornICECS 2020 - 27th IEEE International Conference on Electronics, Circuits and Systems, November 23-25, 2020, Virtual Conference : Proceedings2020 / 4 p https://doi.org/10.1109/ICECS49266.2020.9294881

artikkel kogumikus
7

artikkel kogumikus EST / artikkel kogumikus ENG

FARMUR: fair adversarial retraining to mitigate unfairness in robustnessMousavi, Seyed Ali; Mousavi, Hamid; Daneshtalab, MasoudAdvances in Databases and Information Systems: 27th European Conference, ADBIS 2023, Barcelona, Spain, September 4–7, 2023 : proceedings2023 / p. 133-145 https://doi.org/10.1007/978-3-031-42914-9_10 Conference proceedings at Scopus Article at Scopus

artikkel kogumikus EST / artikkel kogumikus ENG
8

artikkel kogumikus

Gate-level graph representation learning : a step towards the improved stuck-at faults analysisBalakrishnan, Aneesh; Alexandrescu, Dan; Jenihhin, Maksim; Lange, Thomas; Glorieux, MaximilienProceedings of the Twenty Second International Symposium on Quality Electronic Design (ISQED) : Santa Clara, USA, 7-9 April 20212021 / 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
9

artikkel kogumikus EST / artikkel kogumikus ENG

Special session : approximation and fault resiliency of DNN acceleratorsAhmadilivani, Mohammad Hasan; Barbareschi, Mario; Barone, Salvatore; Bosio, Alberto; Daneshtalab, Masoud; Torca, Salvatore Della; Gavarini, Gabriele; Jenihhin, Maksim; Raik, Jaan; Taheri, MahdiProceedings 2023 IEEE 41st VLSI Test Symposium (VTS)2023 / 10 p. : ill https://doi.org/10.1109/VTS56346.2023.10140043 Conference proceeding at Scopus Article at Scopus Article at WOS

artikkel kogumikus EST / artikkel kogumikus ENG
10

artikkel kogumikus EST / artikkel kogumikus ENG

Special session: reliability assessment recipes for DNN acceleratorsAhmadilivani, Mohammad Hasan; Bosio, Alberto; Deveautour, Bastien; Dos Santos, Fernando Fernandes; Guerrero-Balaguera, Juan-David; Jenihhin, Maksim; Kritikakou, Angeliki; Sierra, Robert Limas; Raik, Jaan; Taheri, Mahdi42nd IEEE VLSI Test Symposium, VTS 20242024 / 11 p. : ill https://doi.org/10.1109/VTS60656.2024.10538707 Conference proceedings at Scopus Article at Scopus Article at WOS

artikkel kogumikus EST / artikkel kogumikus ENG
11

artikkel ajakirjas EST / artikkel ajakirjas ENG

A systematic literature review on hardware reliability assessment methods for deep neural networksAhmadilivani, Mohammad Hasan; Taheri, Mahdi; Raik, Jaan; Daneshtalab, Masoud; Jenihhin, MaksimACM Computing Surveys2024 / art. 141 https://doi.org/10.1145/3638242 Journal metrics at Scopus Article at Scopus Journal metrics at WOS Article at WOS

artikkel ajakirjas EST / artikkel ajakirjas ENG
12

artikkel kogumikus EST / artikkel kogumikus ENG

ZED-TTE: Zone embedding and deep neural network based travel time estimation approachOunoughi, Chahinez; Yeferny, Taoufik; Ben Yahia, Sadok2021 International Joint Conference on Neural Networks (IJCNN) : proceedings2021 / 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 12, kuvan 1 - 12

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 artificial neural networks
9. Constrained neural networks
10. convolutional neural networks
11. convolutional neural networks (CNNa)
12. Convolutional Neural Networks (CNNs)
13. Fuzzy neural networks
14. graph neural networks
15. multilayer neural networks
16. neural networks
17. neural-networks
18. pre-trained neural networks
19. recurrent neural networks
20. recurrent neural networks (RNN)
21. deep donor-deep acceptor pairs
22. deep brain stimulation
23. Deep defects
24. deep energy levels
25. deep energy renovation
26. deep eutectic solvents
27. deep fake
28. deep fake recognition
29. deep habits
30. deep heat well
31. deep inference
32. deep layer
33. deep learning
34. deep learning (DL)
35. deep learning classifier
36. deep learning method
37. deep learning methods
38. deep learning model
39. deep learning model quantization
40. deep learning network
41. deep level
42. deep level traps
43. deep levels
44. deep offshore
45. deep packet inspection
46. deep reinforcement learning
47. Deep reinforcing learning SDN
48. deep renovation
49. deep understanding
50. Deep-learning
51. deep-learning (DL)
52. embedded deep learning
53. natural deep eutectic solvents
54. nZEB deep energy renovation
55. nZEB deep renovation
56. artificial neural network
57. Binarized Neural Network (BNN)
58. cascaded forward neural network (CFNN)
59. Convolutional Neural Network
60. convolutional neural network (CNN)
61. feedforward neural network (FFNN)
62. Graph Convolutional Neural Network (GCN)
63. GraphSAGE (Graph Based Neural Network)
64. human neural progenitor cells
65. linear Delta robots neural network based model
66. neural activity
67. neural additive model
68. neural architecture search
69. neural controller
70. neural development
71. neural fuzzy modelling control
72. neural machine translation
73. neural network
74. neural network architecture search
75. neural network controller
76. neural network modelling
77. neural network predictive controller
78. neural oscillations
79. neural processing unit
80. neural stimulation
81. Radial Basis Function Neural Network (RBFNN)
82. recurrent neural network language model
83. Residual Neural Network
84. spiking neural network (SNN)
85. Ternary Neural Network
86. adaptive networks
87. ad-hoc sensor networks security
88. automotive communication networks
89. Bayesian Belief Networks
90. Bayesian networks
91. binary search networks
92. binary sensor networks
93. body area networks
94. body sensor networks
95. cellular networks
96. communication networks
97. complex networks
98. computer networks
99. content distribution networks
100. content-addressable storage networks
101. convolutional networks
102. cross-border communication networks
103. decentralized content-addressable storage (DCAS) networks
104. developmental networks
105. diffusion networks
106. digital twin networks (DTNs)
107. distribution networks
108. district heating networks
109. Dynamic networks
110. dynamics over networks
111. ecological networks
112. economic networks
113. electric power transmission networks
114. electrical distribution networks
115. electrical networks
116. embedded networks/NoC
117. gateways (computer networks)
118. generative adversarial networks
119. geodetic networks
120. global production networks
121. graph attention networks
122. graph convolutional networks
123. heating and cooling networks
124. heating networks
125. hypar-formed networks
126. IaaS cloud networks
127. Impedance-source networks
128. innovation networks
129. IP networks
130. iterative networks
131. knowledge networks
132. location-based social networks
133. logistics networks
134. Long Short-Term Memory Networks (LSTMs)
135. low voltage networks
136. low-voltage networks
137. lumped parameter networks
138. marketing networks
139. metabolic networks and pathways
140. mobile actuator-sensor networks (MAS-Net)
141. multilayer networks
142. NB-IoT networks
143. networks
144. networks coding
145. neuro-fuzzy networks and guided missile
146. non-terrestrial networks
147. observational networks
148. on-body networks (BANs)
149. Online social networks
150. Optical fiber networks
151. optical fibre networks
152. optical networks
153. optimized routing approach for critical and emergency networks
154. optimized routing approach for critical and emergency networks simulations
155. packet switched networks
156. partner networks
157. Passive networks
158. phantom cellular networks
159. private networks
160. production networks
161. Public safety networks
162. public safety networks (PSN)
163. public safety networks (PSNs)
164. reconfigurable scan networks
165. regional innovation networks
166. scale-free networks
167. sensor networks
168. small district heating networks
169. SME networks
170. social networks
171. social networks
172. software defined networks (SDN)
173. sorting networks
174. supply networks
175. transmission networks
176. unreliable wireless networks
177. WBANs (wireless body area networks)
178. wearable wireless networks
179. wearable wireless networks (WWNs)
180. weighted networks
181. wireless ad hoc sensor networks
182. Wireless Body Area Networks
183. wireless body area networks (WBANs)
184. wireless body-to-body networks (BBN)
185. wireless networks
186. wireless sensor networks
187. virtual networks
188. 5G networks
189. 5G networks

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