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UAV-Enabled Maritime IoT D2D Task Offloading: A Potential Game-Accelerated Framework. [PDF]
Sensors (Basel)Li B, Zhao J, Yang T.
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Local rigidity constraints for deformable image registration in CBCT-guided radiotherapy. [PDF]
Med PhysDraper TJW, Zachiu C, Raaymakers BW.
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Smooth and shape-constrained quantile distributed lag models. [PDF]
BiometricsJin Y +3 more
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Dose optimization with fully flexible vertex positioning for LATTICE radiotherapy. [PDF]
Med PhysTong X +7 more
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Dual-Ascent-Inspired Transformer for Compressed Sensing. [PDF]
Sensors (Basel)Lin R, Shen Y, Chen Y.
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High-Resolution and Robust One-Bit Direct-of-Arrival Estimation via Reweighted Atomic Norm Estimation. [PDF]
Sensors (Basel)Li R, Yang J, Dai Z, Lu X, Tan K, Su W.
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Spatially informed reference-free cell-type deconvolution for spatial transcriptomics with SpatialCD. [PDF]
Genome ResVo P, Cui Y.
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Estimation and Inference of Quantile Spatially Varying Coefficient Models Over Complicated Domains. [PDF]
J Am Stat AssocKim M, Wang L, Wang HJ.
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IEEE Transactions on Image Processing, 2023
One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-
Chien-Yu Chiou +3 more
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One-class classification aims to learn one-class models from only in-class training samples. Because of lacking out-of-class samples during training, most conventional deep learning based methods suffer from the feature collapse problem. In contrast, contrastive learning based methods can learn features from only in-class samples but are hard to be end-
Chien-Yu Chiou +3 more
openaire +3 more sources