Results 51 to 60 of about 36,322 (258)
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
Adaptive Neural Network Structure Optimization Algorithm Based on Dynamic Nodes
Current Issues in Molecular Biology, 2022 Large-scale artificial neural networks have many redundant structures, making the network fall into the issue of local optimization and extended training time.
Miao Wang +7 more
doaj +1 more source
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm
Advances in Neural Information Processing Systems 35, 2022 NeurIPS ...
Aidan Good +7 more
openaire +4 more sources
Prune Deep Neural Networks With the Modified
IEEE Access, 2019 Demands to deploy deep neural network (DNN) models on mobile devices and embedded systems have drastically grown in recent years. When transplanting DNN models to such platforms, requirements pertaining to computation and memory use are bottlenecks.
Jing Chang, Jin Sha
doaj +1 more source
Leaftronics: Bio‐Fractal Scaffolds From Leaf Venation for Low‐Waste Electronics
Advanced Materials, EarlyView.“Leaftronics” transforms naturally evolved leaf venation into quasi‐fractal scaffolds for sustainable electronics. Polymer‐infiltrated leaf skeletons can be used to fabricate ultra‐smooth, reflow‐ and thin‐film‐compatible decomposable substrates, while making the same lignocellulose networks conducting results in flexible transparent electrodes.
Rakesh Rajendran Nair +3 more
wiley +1 more source
Complex & Intelligent Systems, 2023 Neural network pruning offers great prospects for facilitating the deployment of deep neural networks on computational resource limited devices.
Hanjing Cheng +5 more
doaj +1 more source
Block-Wisely Supervised Network Pruning with Knowledge Distillation and Markov Chain Monte Carlo
Applied Sciences, 2022 Structural network pruning is an effective way to reduce network size for deploying deep networks to resource-constrained devices. Existing methods mainly employ knowledge distillation from the last layer of network to guide pruning of the whole network,
Huidong Liu +3 more
doaj +1 more source
Scalable Task Planning via Large Language Models and Structured World Representations
Advanced Robotics Research, EarlyView.This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari +4 more
wiley +1 more source
A-Pruning: A Lightweight Pineapple Flowers Counting Network Based on Filter Pruning
SSRN Electronic Journal, 2022 AbstractDuring pineapple cultivation, detecting and counting the number of pineapple flowers in real time and estimating the yield are essential. Deep learning methods are more efficient in real-time performance than traditional manual detection. However, existing deep learning models are characterized by low detection speeds and cannot be applied in ...
Guoyan Yu +4 more
openaire +2 more sources
Advanced Robotics Research, EarlyView.Automated poultry processing lines still rely on humans to lift slippery, easily bruised carcasses onto a shackle conveyor. Deformability, anatomical variance, and hygiene rules make conventional suction and scripted motions unreliable. We present ChicGrasp, an end‐to‐end hardware‐software co‐designed imitation learning framework, to offer a ...
Amirreza Davar +8 more
wiley +1 more source