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Access"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1613\/jair.301","article-title":"Reinforcement learning: a survey","volume":"4","author":"Kaelbling","year":"1996","journal-title":"J Artif Intell Res"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"25396","DOI":"10.1109\/TITS.2022.3145815","article-title":"Multi-attention DenseNet: a scattering medium imaging optimization framework for visual data pre-processing of autonomous driving systems","volume":"23","author":"Liu","year":"2022","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref19","first-page":"1","article-title":"PointNet++: deep hierarchical feature learning on point sets in a metric space","volume":"30","author":"Qi","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"ref20","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"5240","article-title":"Rangevit: towards vision transformers for 3D semantic segmentation in autonomous driving","author":"Ando","year":"2023 Jun 17\u201324"},{"key":"ref21","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556. 2014."},{"key":"ref22","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016 Jun 27\u201330"},{"key":"ref23","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"Szegedy","year":"2017 Feb 4\u20139"},{"key":"ref24","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"4700","article-title":"Densely connected convolutional networks","author":"Huang","year":"2017 Jul 21\u201326"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"3412","DOI":"10.1109\/TNNLS.2020.3015992","article-title":"Deep learning for lidar point clouds in autonomous driving: a review","volume":"32","author":"Li","year":"2020","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TITS.2020.3023541","article-title":"Deep learning for image and point cloud fusion in autonomous driving: a review","volume":"23","author":"Cui","year":"2021","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"ref27","series-title":"International Conference on Machine Learning, LCML","first-page":"3734","article-title":"Self-attention graph pooling","author":"Lee","year":"2019 Jun 9\u201315"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"361","DOI":"10.3233\/IDA-226551","article-title":"A grouping feature selection method based on feature interaction","volume":"27","author":"Zhou","year":"2023","journal-title":"Intell Data Anal"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"345","DOI":"10.3233\/IDA-216447","article-title":"A novel feature selection method considering feature interaction in neighborhood rough set","volume":"27","author":"Wang","year":"2023","journal-title":"Intell Data Anal"},{"key":"ref30","doi-asserted-by":"crossref","unstructured":"Shaw P, Uszkoreit J, Vaswani A. 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