Results 61 to 70 of about 36,322 (258)

Rethinking the Value of Network Pruning

CoRR, 2018
Network pruning is widely used for reducing the heavy inference cost of deep models in low-resource settings. A typical pruning algorithm is a three-stage pipeline, i.e., training (a large model), pruning and fine-tuning. During pruning, according to a certain criterion, redundant weights are pruned and important weights are kept to best preserve the ...
Zhuang Liu 0003, Mingjie Sun, Tinghui Zhou, Gao Huang 0001, Trevor Darrell   +4 more
openaire   +3 more sources

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +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, Rose E. Presby, Sara Nakanishi, Pallavi Gaur, Ugur Akcan, Johannes Daugaard Krog, Iben Daugaard Krog, Lipi Das, David S. Johnson, Danny Jamoul, Douglas Jury, Brian Lawson, Jacob Körbelin, Lewis M. Brown, Vilas Menon, Dritan Agalliu, Irene Munk Pedersen   +16 more
wiley   +1 more source

On Edge Pruning of Communication Networks under an Age-of-Information Framework

Algorithms, 2022
Effective non-repetitive routing among nodes in a network is an essential function in communication networks. To achieve that, pruning the links of the network is helpful with the trade-off of making the network less robust in transmitting messages while
Abdalaziz Sawwan, Jie Wu
doaj   +1 more source

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.
openaire   +3 more sources

A Fair Loss Function for Network Pruning

CoRR, 2022
Model pruning can enable the deployment of neural networks in environments with resource constraints. While pruning may have a small effect on the overall performance of the model, it can exacerbate existing biases into the model such that subsets of samples see significantly degraded performance.
Robbie Meyer, Alexander Wong
openaire   +2 more sources

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, Misheng Cai, Zhenghong Wang, Yan Wang, Yuqi Li, Bo Dong, Hewu Wang, Andrew F. Burke, Simona Onori, Stephen J. Harris   +9 more
wiley   +1 more source

Filter Pruning via Measuring Feature Map Information

Sensors, 2021
Neural network pruning, an important method to reduce the computational complexity of deep models, can be well applied to devices with limited resources. However, most current methods focus on some kind of information about the filter itself to prune the
Linsong Shao, Haorui Zuo, Jianlin Zhang, Zhiyong Xu, Jinzhen Yao, Zhixing Wang, Hong Li   +6 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, Marie Celine Lefevre, Alessio Carmignani, Maria Cristina Ceccarelli, Matteo Battaglini, Attilio Marino, Gianni Ciofani   +6 more
wiley   +1 more source

PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries

Advanced Science, EarlyView.
PhosSight is a unified deep‐learning framework for phosphoproteome identification, featured by a phosphorylation‐aware detectability predictor. It improves identification sensitivity in DDA through deep re‐localization and rescoring, accelerates DIA searches by detectability‐guided spectral library pruning, and expands phosphoproteome coverage to ...
Ben Wang, Zhiyuan Cheng, Chengying She, Hongwei Zhao, Jiahui Zhang, Lin Lv, Zhihao Yan, Hongwen Zhu, Lizhuang Liu, Yan Fu, Xinpei Yi   +10 more
wiley   +1 more source