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Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, protein structures, and connectomes.
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Demystifying MMD GANs
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Generative modeling using the Sliced-Wasserstein distance
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Synthesizing realistic neural population activity patterns using generative adversarial networks
M. Molano-Mazon, A. Onken, E. Piasini, and S. Panzeri · 2018
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An empirical study on evaluation metrics of generative adversarial networks
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The unreasonable effectiveness of deep features as a perceptual metric
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A flow-based latent state generative model of neural population responses to natural images
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Spearheading future omics analyses using dyngen, a multi-modal simulator of single cells
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scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
T. Sun, D. Song, W. V. Li, and J. J. Li · 2021
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Interpretable deep learning uncovers cellular properties in label-free live cell images that are predictive of highly metastatic melanoma
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R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 2018
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Inferring single-trial neural population dynamics using sequential auto-encoders
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Deep generative modeling for single-cell transcriptomics
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Geometry based data generation
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Automatic chemical design using a data-driven continuous representation of molecules
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Molecular generative model based on conditional variational autoencoder for de novo molecular design
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A study on the evaluation of generative models
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Tractable dendritic RNNs for reconstructing nonlinear dynamical systems
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GENIE: Higher-order denoising diffusion solvers
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RePaint: Inpainting using denoising diffusion probabilistic models
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
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