Results 241 to 250 of about 788,437 (264)

Robot steering-angle prediction lightweight network based non-local attention and lane guidance. [PDF]

PLoS One
Niu J, Zheng J, Shen C, Gao G, Song G, Liu S, Wang Y.   +6 more
europepmc   +1 more source

Leveraging Machine Learning Classifiers in Transfer Learning for Few-Shot Modulation Recognition. [PDF]

Sensors (Basel)
Li S, Wang Y, Xiong J, Wang X.
europepmc   +1 more source

ReaGP: integrating residual units and attention mechanisms in convolution neural network for genomic prediction. [PDF]

Genet Sel Evol
Li J, Guo P, Zhang Y, Ma H, Zhao Z, Wang Y, Wang Z, Chen Y, Xu L, Zhang L, Gao H, Gao X, Li J, Zhu B.   +13 more
europepmc   +1 more source

A multidimensional data-driven approach to surgical plan optimization and postoperative residual tumor prediction in ovarian cancer. [PDF]

Front Immunol
Yang L, He T, Yu P, Shang C, Wang J, Sun Q, Xie Y, Yang J, Guo H.   +8 more
europepmc   +1 more source

Improved one-dimensional residual network high-voltage DC diagnosis for high-precision fault identification. [PDF]

PLoS One
Li R, Zhang X, Hao W, Wang T.
europepmc   +1 more source

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Learning residual alternating automata

Information and Computation, 2017
Residuality plays an essential role for learning finite automata. While residual deterministic and non-deterministic automata have been understood quite well, fundamental questions concerning alternating automata (AFA) remain open. Recently, Angluin, Eisenstat, and Fisman (2015) have initiated a systematic study of residual AFAs and ...
Berndt, Sebastian, Liśkiewicz, Maciej, Lutter, Matthias, Reischuk, Rüdiger   +3 more
openaire   +2 more sources

Residual Learning for Salient Object Detection

IEEE Transactions on Image Processing, 2020
Recent deep learning based salient object detection methods improve the performance by introducing multi-scale strategies into fully convolutional neural networks (FCNs). The final result is obtained by integrating all the predictions at each scale.
Mengyang Feng, Huchuan Lu, Yizhou Yu
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Learning Residual Color for Novel View Synthesis

IEEE Transactions on Image Processing, 2022
Scene Representation Networks (SRN) have been proven as a powerful tool for novel view synthesis in recent works. They learn a mapping function from the world coordinates of spatial points to radiance color and the scene's density using a fully connected network. However, scene texture contains complex high-frequency details in practice that is hard to
Lei Han, Dawei Zhong, Lin Li, Kai Zheng, Lu Fang   +4 more
openaire   +2 more sources