Results 31 to 40 of about 22,594 (260)
Symmetric Pruning in Quantum Neural Networks
CoRR, 2022 Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state. Despite the significance of ground states preparation (GSP), this task is classically intractable for large-scale Hamiltonians. Quantum neural networks (QNNs), which exert the power of modern quantum machines, have emerged as a leading protocol to conquer ...
Xinbiao Wang +5 more
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Dirichlet Pruning for Neural Network Compression
CoRR, 2020 We introduce Dirichlet pruning, a novel post-processing technique to transform a large neural network model into a compressed one. Dirichlet pruning is a form of structured pruning that assigns the Dirichlet distribution over each layer's channels in convolutional layers (or neurons in fully-connected layers) and estimates the parameters of the ...
Adamczewski, K., Park, M.
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Neural Network-Based Fixed-Complexity Precoder Selection for Multiple Antenna Systems
IEEE Access, 2022 In this paper, we propose a neural network-based precoder selection method for multiple antenna systems that are equipped with maximum likelihood detectors.
Jaekwon Kim, Hyo-Sang Lim
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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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Automatic Pruning for Quantized Neural Networks
CoRR, 2020 Neural network quantization and pruning are two techniques commonly used to reduce the computational complexity and memory footprint of these models for deployment. However, most existing pruning strategies operate on full-precision and cannot be directly applied to discrete parameter distributions after quantization.
Luis Guerra +3 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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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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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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Cyclical Pruning for Sparse Neural Networks
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Current methods for pruning neural network weights iteratively apply magnitude-based pruning on the model weights and re-train the resulting model to recover lost accuracy. In this work, we show that such strategies do not allow for the recovery of erroneously pruned weights.
Suraj Srinivas +5 more
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