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www.assemblyai.com | ||
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www.nicktasios.nl
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| | | | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In the third, and last, post, | |
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xcorr.net
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| | | | | 2022 was the year of generative AI models: DALL-E 2, MidJourney, Stable Diffusion, and Imagen all showed that it's possible to generate grounded, photorealistic images. These generative AIs are instances of conditional denoising diffusion probabilistic models, or DDPMs. Despite these flashy applications, DDPMs have thus far had little impact on neuroscience. An oil painting of... | |
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sander.ai
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| | | | | Perspectives on diffusion, or how diffusion models are autoencoders, deep latent variable models, score function predictors, reverse SDE solvers, flow-based models, RNNs, and autoregressive models, all at once! | |
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nhigham.com
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| | | A norm on $latex \mathbb{C}^{m \times n}$ is unitarily invariant if $LATEX \|UAV\| = \|A\|$ for all unitary $latex U\in\mathbb{C}^{m \times m}$ and $latex V\in\mathbb{C}^{n\times n}$ and for all $latex A\in\mathbb{C}^{m \times n}$. One can restrict the definition to real matrices, though the term unitarily invariant is still typically used. Two widely used matrix norms... | ||
