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Large language models (LLMs) based on the generative pre-training transformer (GPT) have demonstrated remarkable effectiveness across a diverse range of downstream tasks.
A computer oriented geodetic data base and a new technique in file sequencing
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Decoupled weight decay regularization
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Attention is all you need
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Jiaxin Li, Ben M Chen, and Gim Hee Lee · 2018
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Pointcnn: Convolution on x-transformed points
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Generating wikipedia by summarizing long sequences
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Foldingnet: Point cloud auto-encoder via deep grid deformation
Yaoqing Yang, Chen Feng, Yiru Shen, and Dong Tian · 2018
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Abdullah Hamdi, Silvio Giancola, and Bernard Ghanem · 2021
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Spatiotemporal contrastive video representation learning
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Geometric back-projection network for point cloud classification
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Detecting formal thought disorder by deep contextualized word representations
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Self-supervised pretraining of 3d features on any point-cloud
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Partnet: A large-scale benchmark for fine-grained and hierarchical part-level 3d object understanding
Kaichun Mo, Shilin Zhu, Angel X Chang, Li Yi, Subarna Tripathi, Leonidas J Guibas, and Hao Su · 2019
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Language models are unsupervised multitask learners
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Masked discrimination for self-supervised learning on point clouds
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Masked autoencoders for point cloud self-supervised learning
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Pointnext: Revisiting pointnet++ with improved training and scaling strategies
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P2p: Tuning pre-trained image models for point cloud analysis with point-to-pixel prompting
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Ulip: Learning unified representation of language, image and point cloud for 3d understanding
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Point-bert: Pre-training 3d point cloud transformers with masked point modeling
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Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training
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Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining
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