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cyclostationary.blog | ||
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blog.shakirm.com
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| | | | | Memory, the ways in which we remember and recall past experiences and data to reason about future events, is a termused frequently in current literature. All models in machine learning consist of... | |
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yang-song.net
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| | | | | This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... | |
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bartwronski.com
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| | | | | Recently, numerous academic papers in the machine learning / computer vision / image processing domains (re)introduce and discuss a "frequency loss function" or "spectral loss" - and while for many it makes sense and nicely improves achieved results, some of them define or use it wrongly. The basic idea is - instead of comparing pixels... | |
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cp4space.hatsya.com
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| | | At the end of the recent post on a combinatorial proof of Houston's identity, I ended with the following paragraph: This may seem paradoxical, but there's an analogous situation in fast matrix multiplication: the best known upper bound for the tensor rank of 4-by-4 matrix multiplication is 49, by applying two levels of Strassen's algorithm,... | ||
