Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Eugenio Culurciello"'
Autor:
Eugenio Culurciello, Thomas Molnar
Publikováno v:
International Journal of Artificial Intelligence & Applications. 11:1-15
Capsule Networks (CapsNets) have been proposed as an alternative to Convolutional Neural Networks (CNNs). This paper showcases how CapsNets are more capable than CNNs for autonomous agent exploration of realistic scenarios. In real world navigation,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c486a40d123c1344b5b915cf1ffe88db
https://doi.org/10.5121/ijaia.2020.11101
https://doi.org/10.5121/ijaia.2020.11101
Autor:
Michael J. Mlodzianoski, Sheng Liu, Abhishek Chaurasia, Fang Huang, Donghan Ma, Peiyi Zhang, Eugenio Culurciello
Publikováno v:
Nature methods
A fluorescent emitter simultaneously transmits its identity, location, and cellular context through its emission pattern. We developed smNet, a deep neural network for multiplexed single-molecule analysis to enable retrieving such information with hi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27b9c214c722c749c9068ecf9a55ab2c
http://europepmc.org/articles/PMC6624853
http://europepmc.org/articles/PMC6624853
Publikováno v:
LCTES
Deep Neural Networks (DNNs) are the algorithm of choice for image processing applications. DNNs present highly parallel workloads that lead to the emergence of custom hardware accelerators. Deep Learning (DL) models specialized in different tasks req
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::175ff6169ba3b70da6a9f9d32f70eca8
https://doi.org/10.1145/3299710.3211333
https://doi.org/10.1145/3299710.3211333
Publikováno v:
CVPR Workshops
High-dimensional always-changing environments constitute a hard challenge for current reinforcement learning techniques. Artificial agents, nowadays, are often trained off-line in very static and controlled conditions in simulation such that training
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::404062f45f99c6c8a717b7e23e419989
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 28:1572-1583
Deep convolutional neural networks (DCNNs) have become a very powerful tool in visual perception. DCNNs have applications in autonomous robots, security systems, mobile phones, and automobiles, where high throughput of the feedforward evaluation phas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be272ad4cd435a4ec1d454a62f4db026
https://doi.org/10.1109/tnnls.2016.2545298
https://doi.org/10.1109/tnnls.2016.2545298
Publikováno v:
Advances in Visual Computing ISBN: 9783030337193
ISVC (1)
ISVC (1)
Exploring novel environments for a specific target poses the challenge of how to adequately provide positive external rewards to an artificial agent. In scenarios with sparse external rewards, a reinforcement learning algorithm often cannot develop a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c8d341f06f49ddd90cb6ca56f293e4d6
https://doi.org/10.1007/978-3-030-33720-9_20
https://doi.org/10.1007/978-3-030-33720-9_20
Publikováno v:
2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2).
Deep Neural Networks (DNNs) are the current state of the art for various tasks such as object detection, natural language processing and semantic segmentation. These networks are massively parallel, hierarchical models with each level of hierarchy pe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a930d88fcdbde67fc2e858a7995dbd90
https://doi.org/10.1109/emc2.2018.00010
https://doi.org/10.1109/emc2.2018.00010
Publikováno v:
2018 1st Workshop on Energy Efficient Machine Learning and Cognitive Computing for Embedded Applications (EMC2).
Deep Neural Networks (DNNs) are widely used in various applications including image classification, semantic segmentation and natural language processing. Various DNN models were developed to achieve high accuracy on different tasks. Efficiently mapp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07f4a4734bc307e97b267aee4c11f452
https://doi.org/10.1109/emc2.2018.00013
https://doi.org/10.1109/emc2.2018.00013
Autor:
Eugenio Culurciello
Publikováno v:
TIML@ISCA
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a87949a3bfa2c2ab6f4f28096b8478c0
https://doi.org/10.1145/3149166.3149178
https://doi.org/10.1145/3149166.3149178
Publikováno v:
ISCAS
Deep learning is becoming increasingly popular for a wide variety of applications including object detection, classification, semantic segmentation and natural language processing. Convolutional neural networks (CNNs) are a type of deep neural networ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9250349fbbac39b1ff54e6108ef906c4
https://doi.org/10.1109/iscas.2017.8050809
https://doi.org/10.1109/iscas.2017.8050809