BUSSOLINO PANGERZ, BEATRICE
 Distribuzione geografica
Continente #
EU - Europa 237
NA - Nord America 49
AS - Asia 5
OC - Oceania 2
Totale 293
Nazione #
IT - Italia 111
GB - Regno Unito 84
US - Stati Uniti d'America 48
IE - Irlanda 17
DE - Germania 6
CZ - Repubblica Ceca 4
FR - Francia 3
RU - Federazione Russa 3
AT - Austria 2
CH - Svizzera 2
NZ - Nuova Zelanda 2
SE - Svezia 2
CA - Canada 1
CN - Cina 1
FI - Finlandia 1
IR - Iran 1
JP - Giappone 1
MY - Malesia 1
PT - Portogallo 1
RO - Romania 1
SG - Singapore 1
Totale 293
Città #
Southend 84
Torino 34
Turin 24
Dublin 17
Houston 9
Fairfield 5
Fremont 4
Bottanuco 3
Macerata 3
Milan 3
Rome 3
San Mateo 3
Basking Ridge 2
Bremen 2
Monmouth Junction 2
Novokuznetsk 2
Pray 2
Seattle 2
Tauranga 2
Andover 1
Ann Arbor 1
Aosta 1
Ardabil 1
Barcelos 1
Barge 1
Blansko 1
Borås 1
Buffalo 1
Catania 1
Chicago 1
Dallas 1
Florence 1
Hangzhou 1
Helsinki 1
Kuala Lumpur 1
Mestre 1
Mirano 1
Nichelino 1
Palo Alto 1
Pellezzano 1
Pescara 1
San Donato Milanese 1
San Giuliano Milanese 1
Taranto 1
Tokyo 1
Toronto 1
Vienna 1
Villamar 1
Wilmington 1
Zurich 1
Totale 237
Nome #
An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks, file e384c432-8140-d4b2-e053-9f05fe0a1d67 52
Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead, file e384c432-c54b-d4b2-e053-9f05fe0a1d67 50
NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks, file e384c432-913a-d4b2-e053-9f05fe0a1d67 43
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks, file e384c432-4159-d4b2-e053-9f05fe0a1d67 38
A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress, file e384c432-d7cf-d4b2-e053-9f05fe0a1d67 34
FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks, file e384c432-3cde-d4b2-e053-9f05fe0a1d67 31
Techniques and Optimization Strategies for Efficient Hardware Acceleration of Neural Networks: Tap-Wisely-Quantized Winograd Algorithm and Capsule Networks, file c1694116-bb13-424e-b725-6bc0d43cd22b 11
RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks, file a2c21ec6-1867-4026-9951-e1b075838e63 6
NLCMAP: A Framework for the Efficient Mapping of Non-Linear Convolutional Neural Networks on FPGA Accelerators, file 10595e48-4336-4d46-9dae-dcb9c98eeb2a 5
Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations, file 399149ab-7003-4e8f-8946-d48b30cf607e 5
FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks, file e384c432-3244-d4b2-e053-9f05fe0a1d67 5
NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks, file e384c432-c25e-d4b2-e053-9f05fe0a1d67 4
Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile, file d3421821-6795-4665-8aaa-d34334624983 3
A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress, file e384c432-de77-d4b2-e053-9f05fe0a1d67 3
Techniques and Optimization Strategies for Efficient Hardware Acceleration of Neural Networks: Tap-Wisely-Quantized Winograd Algorithm and Capsule Networks, file 9cf324d6-8b94-4cde-9a1b-9646286804ba 2
Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks, file e384c432-47af-d4b2-e053-9f05fe0a1d67 2
Totale 294
Categoria #
all - tutte 664
article - articoli 214
book - libri 0
conference - conferenze 366
curatela - curatele 0
other - altro 0
patent - brevetti 0
selected - selezionate 0
volume - volumi 0
Totale 1.244


Totale Lug Ago Sett Ott Nov Dic Gen Feb Mar Apr Mag Giu
2020/2021112 2 1 4 10 16 22 3 7 14 4 18 11
2021/2022116 8 11 8 15 7 13 1 8 8 12 17 8
2022/202352 1 4 12 4 5 13 9 0 1 1 2 0
2023/202414 1 2 3 0 0 0 1 1 1 1 4 0
Totale 294