Results 21 to 30 of about 6,207,310 (278)
Neural Architecture Search for Light-weight Medical Image Segmentation Network [PDF]
Jisuanji kexue, 2022 Most of the existing medical image segmentation models with excellent performance are manually designed by domain experts.The design process usually requires a lot of professional knowledge and repeated experiments.In addition,the over complex ...
ZHANG Fu-chang, ZHONG Guo-qiang, MAO Yu-xu
doaj +1 more source
Differentially-private Federated Neural Architecture Search [PDF]
, 2020 Neural architecture search, which aims to automatically search for architectures (e.g., convolution, max pooling) of neural networks that maximize validation performance, has achieved remarkable progress recently. In many application scenarios, several parties would like to collaboratively search for a shared neural architecture by leveraging data from
Ishika Singh +5 more
openaire +2 more sources
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
Latency-Aware Differentiable Neural Architecture Search
CoRR, 2020 Differentiable neural architecture search methods became popular in recent years, mainly due to their low search costs and flexibility in designing the search space. However, these methods suffer the difficulty in optimizing network, so that the searched network is often unfriendly to hardware.
Yuhui Xu 0002 +6 more
openaire +3 more sources
Topology-Sensitive Neural Architecture Search for Language Modeling
IEEE Access, 2021 Recently Neural Architecture Search has drawn interest from researchers because of its ability to learn neural network architectures from data automatically.
Quan Du +4 more
doaj +1 more source
Gradient Descent Effects on Differential Neural Architecture Search: A Survey [PDF]
IEEE Access, 2021 Gradient Descent, an effective way to search for the local minimum of a function, can minimize training and validation loss of neural architectures and also be incited in an appropriate order to decrease the searching cost of neural architecture search.
Santanu Santra +2 more
openaire +3 more sources
NDARTS: A Differentiable Architecture Search Based on the Neumann Series
Algorithms, 2023 Neural architecture search (NAS) has shown great potential in discovering powerful and flexible network models, becoming an important branch of automatic machine learning (AutoML).
Xiaoyu Han +3 more
doaj +1 more source
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures
CoRR, 2021 This study aims at making the architecture search process more adaptive for one-shot or online training. It is extended from the existing study on differentiable neural architecture search, and we made the backbone architecture transformable rather than fixed during the training process.
Renlong Jie, Junbin Gao
openaire +2 more sources
FastDARTSDet: Fast Differentiable Architecture Joint Search on Backbone and FPN for Object Detection
Applied Sciences, 2022 Neural architecture search (NAS) is a popular branch of automatic machine learning (AutoML), which aims to search for efficient network structures.
Chunxian Wang +7 more
doaj +1 more source
IEEE Access, 2022 The progress devoted to improving the performance of neural networks has come at a high price in terms of cost and experience. Fortunately, the emergence of Neural Architecture Search improves the speed of network design, but most excellent works only ...
Hui Wei +3 more
doaj +1 more source