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Machine learning models are commonly trained end-to-end and in a supervised setting, using paired (input, output) data.
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Geometric integration theory
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The australian imaging, biomarkers and lifestyle (AIBL) study of aging: methodology and baseline characteristics of 1112 individuals recruited for a longitudinal study of Alzheimer’s disease
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Stochastic backpropagation and approximate inference in deep generative models
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Adam: A method for stochastic optimization
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MIMIC-III, a freely accessible critical care database
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beta-VAE: Learning basic visual concepts with a constrained variational framework
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Conditional image generation with pixelcnn decoders
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Pixel recurrent neural networks
Aaron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
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Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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The Parkinson’s progression markers initiative (PPMI)–establishing a PD biomarker cohort
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Identification of autism spectrum disorder using deep learning and the ABIDE dataset
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Feedback network for image super-resolution
Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, and Wei Wu · 2019
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Neural discrete representation learning
Aaron Van Den Oord, Oriol Vinyals, et al · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al · 2017
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Compressed sensing using generative models
Ashish Bora, Ajil Jalal, Eric Price, and Alexandros G Dimakis · 2017
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Elements of causal inference
Jonas Peters, Dominik Janzing, and Bernhard Schölkopf · 2017
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Anatomical priors in convolutional networks for unsupervised biomedical segmentation
Adrian V Dalca, John Guttag, and Mert R Sabuncu · 2018
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Large scale gan training for high fidelity natural image synthesis
Andrew Brock, Jeff Donahue, and Karen Simonyan · 2018
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Neural ordinary differential equations
Ricky TQ Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Free-form image inpainting with gated convolution
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang · 2019
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Image2stylegan: How to embed images into the stylegan latent space?
Rameen Abdal, Yipeng Qin, and Peter Wonka · 2019
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Minimal achievable sufficient statistic learning
Milan Cvitkovic and Günther Koliander · 2019
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Exploiting deep generative prior for versatile image restoration and manipulation
Xingang Pan, Xiaohang Zhan, Bo Dai, Dahua Lin, Chen Change Loy, and Ping Luo · 2020
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Invertible generative models for inverse problems: mitigating representation error and dataset bias
Muhammad Asim, Max Daniels, Oscar Leong, Ali Ahmed, and Paul Hand · 2020
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Srflow: Learning the super-resolution space with normalizing flow
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PULSE: Self-supervised photo upsampling via latent space exploration of generative models
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Image2stylegan++: How to edit the embedded images?
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Encoding in style: a stylegan encoder for image-to-image translation
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Mimicgan: Robust projection onto image manifolds with corruption mimicking
Rushil Anirudh, Jayaraman J Thiagarajan, Bhavya Kailkhura, and Peer-Timo Bremer · 2020
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Nerf: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Neuromorphologicaly-preserving volumetric data encoding using VQ-VAE
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Prior image-constrained reconstruction using style-based generative models
Varun A Kelkar and Mark A Anastasio · 2021
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Composing normalizing flows for inverse problems
Jay Whang, Erik Lindgren, and Alex Dimakis · 2021
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