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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 architecture search
65. neural controller
66. neural development
67. neural fuzzy modelling control
68. neural machine translation
69. neural network
70. neural network architecture search
71. neural network controller
72. neural network predictive controller
73. neural oscillations
74. neural processing unit
75. neural stimulation
76. Radial Basis Function Neural Network (RBFNN)
77. recurrent neural network language model
78. spiking neural network (SNN)
79. Ternary Neural Network
80. adaptive networks
81. ad-hoc sensor networks security
82. automotive communication networks
83. Bayesian Belief Networks
84. Bayesian networks
85. binary search networks
86. binary sensor networks
87. body area networks
88. body sensor networks
89. cellular networks
90. communication networks
91. complex networks
92. computer networks
93. content distribution networks
94. content-addressable storage networks
95. convolutional networks
96. cross-border communication networks
97. decentralized content-addressable storage (DCAS) networks
98. developmental networks
99. diffusion networks
100. digital twin networks (DTNs)
101. distribution networks
102. district heating networks
103. Dynamic networks
104. dynamics over networks
105. ecological networks
106. economic networks
107. electric power transmission networks
108. electrical distribution networks
109. electrical networks
110. embedded networks/NoC
111. gateways (computer networks)
112. generative adversarial networks
113. geodetic networks
114. global production networks
115. graph attention networks
116. graph convolutional networks
117. heating networks
118. hypar-formed networks
119. IaaS cloud networks
120. Impedance-source networks
121. innovation networks
122. IP networks
123. iterative networks
124. knowledge networks
125. location-based social networks
126. logistics networks
127. low voltage networks
128. low-voltage networks
129. lumped parameter networks
130. marketing networks
131. metabolic networks and pathways
132. mobile actuator-sensor networks (MAS-Net)
133. multilayer networks
134. NB-IoT networks
135. networks
136. networks coding
137. neuro-fuzzy networks and guided missile
138. non-terrestrial networks
139. observational networks
140. on-body networks (BANs)
141. Online social networks
142. Optical fiber networks
143. optical fibre networks
144. optical networks
145. optimized routing approach for critical and emergency networks
146. optimized routing approach for critical and emergency networks simulations
147. packet switched networks
148. partner networks
149. Passive networks
150. phantom cellular networks
151. private networks
152. production networks
153. Public safety networks
154. public safety networks (PSN)
155. public safety networks (PSNs)
156. reconfigurable scan networks
157. regional innovation networks
158. scale-free networks
159. sensor networks
160. small district heating networks
161. SME networks
162. social networks
163. social networks
164. software defined networks (SDN)
165. sorting networks
166. supply networks
167. transmission networks
168. unreliable wireless networks
169. WBANs (wireless body area networks)
170. wearable wireless networks
171. wearable wireless networks (WWNs)
172. weighted networks
173. wireless ad hoc sensor networks
174. Wireless Body Area Networks
175. wireless body area networks (WBANs)
176. wireless body-to-body networks (BBN)
177. wireless sensor networks
178. virtual networks
179. 5G networks
180. 5G networks

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