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Autoregressive models have proven to be very powerful in NLP text generation tasks and lately have gained popularity for image generation as well.
Geometric modeling using octree encoding
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The shape variational autoencoder: A deep generative model of part-segmented 3d objects
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Octnet: Learning deep 3d representations at high resolutions
Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger · 2017
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Maxim Tatarchenko, Alexey Dosovitskiy, and Thomas Brox · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Language models are unsupervised multitask learners
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Transformers are rnns: Fast autoregressive transformers with linear attention
A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret · 2020
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Polygen: An autoregressive generative model of 3d meshes
Charlie Nash, Yaroslav Ganin, S. M. Ali Eslami, and Peter W. Battaglia · 2020
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Pointgrow: Autoregressively learned point cloud generation with self-attention
Yongbin Sun, Yue Wang, Ziwei Liu, Joshua E. Siegel, and Sanjay E. Sarma · 2020
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Adaptive o-cnn: A patch-based deep representation of 3d shapes
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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Point cloud GAN
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A convolutional decoder for point clouds using adaptive instance normalization
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Yi Tay, Mostafa Dehghani, Dara Bahri, and Donald Metzler · 2020
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Fast transformers with clustered attention
A. Vyas, A. Katharopoulos, and F. Fleuret · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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Cvt: Introducing convolutions to vision transformers
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