Results 61 to 70 of about 22,594 (260)
Progressive multi-level distillation learning for pruning network
Complex & Intelligent Systems, 2023 Although the classification method based on the deep neural network has achieved excellent results in classification tasks, it is difficult to apply to real-time scenarios because of high memory footprints and prohibitive inference times.
Ruiqing Wang +9 more
doaj +1 more source
Roulette: A Pruning Framework to Train a Sparse Neural Network From Scratch
IEEE Access, 2021 Due to space and inference time restrictions, finding an efficient and sparse sub-network from a dense and over-parameterized network is critical for deploying neural networks on edge devices.
Qiaoling Zhong +3 more
doaj +1 more source
Modulation of miR‐23b Wnt/β‐catenin Axis Strengthens Endothelial Barrier Properties
Advanced Science, EarlyView.Early blood‐brain barrier (BBB) disruption contributes to stroke and CNS disease pathology. miR‐23b was identified as a regulator of BBB integrity in brain endothelial cells. Inhibition of miR‐23b enhanced barrier‐associated properties, promoted repair‐related signaling, and reduced BBB leakage in experimental stroke models, supporting further ...
Victor Anthony Martinez +16 more
wiley +1 more source
A Filter Pruning Method of CNN Models Based on Feature Maps Clustering
Applied Sciences, 2022 The convolutional neural network (CNN) has been widely used in the field of self-driving cars. To satisfy the increasing demand, the deeper and wider neural network has become a general trend.
Zhihong Wu +5 more
doaj +1 more source
Performance–Complexity Trade‐Offs in Battery Lifetime Prediction with Task‐Aware Transformers
Advanced Science, EarlyView.FAST‐BatPro integrates convolutional feature extraction, flash Attention, and sparse attention for efficient battery lifetime prediction. Using limited early‐cycle data across multiple chemistries and operating conditions, it achieves robust accuracy while reducing inference latency, computational cost, and energy consumption.
Jingyuan Zhao +9 more
wiley +1 more source
Cluster-Based Structural Redundancy Identification for Neural Network Compression
Entropy, 2022 The increasingly large structure of neural networks makes it difficult to deploy on edge devices with limited computing resources. Network pruning has become one of the most successful model compression methods in recent years.
Tingting Wu +3 more
doaj +1 more source
Smart Nanotechnologies for Multimodal Neuromodulation and Brain Interfacing
Advanced Science, EarlyView.Recent advances in smart nanotechnologies are expanding the toolbox for brain interfacing, from wireless neuromodulation and high‐resolution sensing to targeted delivery within the central nervous system. By combining responsive nanomaterials with bioinspired design, these platforms enable multimodal interactions with neurons and glia, while also ...
Tommaso Curiale +6 more
wiley +1 more source
Vojnotehnički Glasnik, 2015 This paper presents the training, testing and pruning of a feedforward neural network with one hidden layer that was used for the prediction of the vowel ”a”.
Danijela D. Protić
doaj +1 more source
Photonic‐Enabled Energy‐Efficient Transparent Neuromorphic Computing Devices: A Review
Advanced Science, EarlyView.Transparent photonic neuromorphic computing devices merge optics and brain‐inspired computing to overcome von Neumann bottlenecks with ultrafast, low‐energy processing. By exploiting transparent oxides, 2D materials, phase‐change materials, and hybrid heterostructures, these platforms enable photonic synapses, memory, and logic for see‐through edge ...
Shuvaraj Ghosh +8 more
wiley +1 more source
To Prune or not to Prune: A Chaos-Causality Approach to Principled Pruning of Dense Neural Networks
CoRR, 2023 Reducing the size of a neural network (pruning) by removing weights without impacting its performance is an important problem for resource-constrained devices. In the past, pruning was typically accomplished by ranking or penalizing weights based on criteria like magnitude and removing low-ranked weights before retraining the remaining ones.
Rajan Sahu +4 more
openaire +2 more sources