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Denoising diffusion models enable conditional generation and density modeling of complex relationships like images and text.
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Comprehensive discovery of subsample gene expression components by information explanation: therapeutic implications in cancer
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The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery
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On variational bounds of mutual information
Ben Poole, Sherjil Ozair, Aaron Van Den Oord, Alex Alemi, and George Tucker · 2019
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Score-based generative modeling through stochastic differential equations
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Multimodal datasets: misogyny, pornography, and malignant stereotypes
Abeba Birhane, Vinay Uday Prabhu, and Emmanuel Kahembwe · 2021
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Openclip, July 2021
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
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Diederik P Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
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Redundant information neural estimation
Michael Kleinman, Alessandro Achille, Stefano Soatto, and Jonathan C Kao · 2021
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Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2021
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Kyle Reing, Greg Ver Steeg, and Aram Galstyan · 2021
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
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ediffi: Text-to-image diffusion models with an ensemble of expert denoisers
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A novel approach to the partial information decomposition
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Information-theoretic diffusion
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Diffusion model is secretly a training-free open vocabulary semantic segmenter, 2023
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De novo design of protein structure and function with rfdiffusion
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