Results 41 to 50 of about 79,498 (272)
Frontiers in Neuroscience, 2019 Inspired from the computational efficiency of the biological brain, spiking neural networks (SNNs) emulate biological neural networks, neural codes, dynamics, and circuitry.
Yuhan Shi +4 more
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IEEE Access, 2023 With the improvement of remote sensing image resolution, remote sensing image scene classification has become a major difficulty in the research of remote sensing Urban green space spatial layout and site selection.
Ding Fan +4 more
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A Probabilistic Approach to Neural Network Pruning
CoRR, 2021 Neural network pruning techniques reduce the number of parameters without compromising predicting ability of a network. Many algorithms have been developed for pruning both over-parameterized fully-connected networks (FCNs) and convolutional neural networks (CNNs), but analytical studies of capabilities and compression ratios of such pruned sub ...
Xin Qian, Diego Klabjan
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Sensors, 2023 Automation in agriculture can save labor and raise productivity. Our research aims to have robots prune sweet pepper plants automatically in smart farms.
Truong Thi Huong Giang, Young-Jae Ryoo
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Image Super-Resolution Reconstruction Algorithm Based on Sparse Neural Network [PDF]
Jisuanji gongcheng, 2022 Many deep learning-based image super-resolution reconstruction algorithms improve the overall feature expression ability of a network by extending the depth of the network.However, excessively extending the depth of the network causes the model to be ...
LI Haomin, LI Guangping
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Combine-Net: An Improved Filter Pruning Algorithm
Information, 2021 The powerful performance of deep learning is evident to all. With the deepening of research, neural networks have become more complex and not easily generalized to resource-constrained devices.
Jinghan Wang, Guangyue Li, Wenzhao Zhang
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Partition Pruning: Parallelization-Aware Pruning for Dense Neural Networks [PDF]
2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), 2020 Parameters of recent neural networks require a huge amount of memory. These parameters are used by neural networks to perform machine learning tasks when processing inputs. To speed up inference, we develop Partition Pruning, an innovative scheme to reduce the parameters used while taking into consideration parallelization. We evaluated the performance
Shahhosseini, Sina +3 more
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IEEE Access, 2023 Train Operators can improve railway passengers’ service quality and traffic management by accurately predicting travel arrangements and delays. Precise prediction of train delays is vital for creating feasible scheduled timetables.
Veronica A. Boateng, Bo Yang
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Hyperparameter Optimization with Neural Network Pruning
CoRR, 2022 Since the deep learning model is highly dependent on hyperparameters, hyperparameter optimization is essential in developing deep learning model-based applications, even if it takes a long time. As service development using deep learning models has gradually become competitive, many developers highly demand rapid hyperparameter optimization algorithms.
Kangil Lee, Junho Yim
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Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference
Frontiers in Artificial Intelligence, 2021 Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data throughput and reduced energy consumption.
Benjamin Hawks +6 more
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