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Recurrent neural networks (RNN) are used in many real-world text and speech applications. They include complex modules such as recurrence, exponential-based activation, gate interaction, unfoldable normalization, bi-directional dependence, and attent
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2993d6310e9086edb102424cc4e511ec
Publikováno v:
CRV
Pruning is one of the most effective model reduction techniques. Deep networks require massive computation and such models need to be compressed to bring them on edge devices. Most existing pruning techniques are focused on vision-based models like c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::eaad3cd8b30f7a57a39c247b3dc1e93e
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030272715
ICIAR (2)
ICIAR (2)
Deep neural networks (DNNs) have demonstrated success for many supervised learning tasks, ranging from voice recognition, object detection, to image classification. However, their increasing complexity might yield poor generalization error that make
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b4c06740f3fc9389f8cf14a2954a146e
https://doi.org/10.1007/978-3-030-27272-2_1
https://doi.org/10.1007/978-3-030-27272-2_1
Publikováno v:
CVPR Workshops
Neural network models are resource hungry. It is difficult to deploy such deep networks on devices with limited resources, like smart wearables, cellphones, drones, and autonomous vehicles. Low bit quantization such as binary and ternary quantization
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::914253be1f844076a6cd54c01462dc6f