Adaptive CNN filter pruning using global importance metric
The depth and width of CNNs have increased over the years so as to learn a better
representation of the input–output mapping of a dataset. However, a significant amount of …
representation of the input–output mapping of a dataset. However, a significant amount of …
A sparse conjugate gradient adaptive filter
In this letter, we propose a novel conjugate gradient (CG) adaptive filtering algorithm for
online estimation of system responses that admit sparsity. Specifically, the Sparsity …
online estimation of system responses that admit sparsity. Specifically, the Sparsity …
On the optimality of backward regression: Sparse recovery and subset selection
S Ament, C Gomes - ICASSP 2021-2021 IEEE International …, 2021 - ieeexplore.ieee.org
Sparse recovery and subset selection are fundamental problems in varied communities,
including signal processing, statistics and machine learning. Herein, we focus on an …
including signal processing, statistics and machine learning. Herein, we focus on an …
Research on Key Technologies of Power Equipment Target Recognition Based on Visual Large Model
F Zhao, K Xie, S Li, L Zhao, C Long… - 2024 IEEE 7th …, 2024 - ieeexplore.ieee.org
Based on high-precision pre-training segmentation large model training technology, this
paper uses model fine-tuning technology, pre-training segmentation large model training …
paper uses model fine-tuning technology, pre-training segmentation large model training …
[图书][B] A Family of Sparsity-Promoting Gradient Descent Algorithms Based on Sparse Signal Recovery
CH Lee - 2020 - search.proquest.com
Sparsity has played an important role in numerous signal processing systems. By leveraging
sparse representations of signals, many batch estimation algorithms and methods that are …
sparse representations of signals, many batch estimation algorithms and methods that are …