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

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