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  2. Variational autoencoder - Wikipedia

    en.wikipedia.org/wiki/Variational_autoencoder

    A variational autoencoder is a generative model with a prior and noise distribution respectively. Usually such models are trained using the expectation-maximization meta-algorithm (e.g. probabilistic PCA , (spike & slab) sparse coding).

  3. Reparameterization trick - Wikipedia

    en.wikipedia.org/wiki/Reparameterization_trick

    The reparameterization trick (aka "reparameterization gradient estimator") is a technique used in statistical machine learning, particularly in variational inference, variational autoencoders, and stochastic optimization.

  4. Autoencoder - Wikipedia

    en.wikipedia.org/wiki/Autoencoder

    An autoencoder has two main parts: an encoder that maps the message to a code, and a decoder that reconstructs the message from the code. An optimal autoencoder would perform as close to perfect reconstruction as possible, with "close to perfect" defined by the reconstruction quality function d {\displaystyle d} .

  5. Diffusion model - Wikipedia

    en.wikipedia.org/wiki/Diffusion_model

    The encoder-decoder pair is most often a variational autoencoder (VAE). Architectural improvements ... "Guidance: a cheat code for diffusion models". 26 May 2022.

  6. Stable Diffusion - Wikipedia

    en.wikipedia.org/wiki/Stable_Diffusion

    Stable Diffusion consists of 3 parts: the variational autoencoder (VAE), U-Net, and an optional text encoder. [17] The VAE encoder compresses the image from pixel space to a smaller dimensional latent space , capturing a more fundamental semantic meaning of the image. [ 16 ]

  7. Variational Bayesian methods - Wikipedia

    en.wikipedia.org/wiki/Variational_Bayesian_methods

    Variational Bayesian methods are a family of techniques for approximating intractable integrals arising in Bayesian inference and machine learning.They are typically used in complex statistical models consisting of observed variables (usually termed "data") as well as unknown parameters and latent variables, with various sorts of relationships among the three types of random variables, as ...

  8. Guitar chord - Wikipedia

    en.wikipedia.org/wiki/Guitar_chord

    The implementation of chords using particular tunings is a defining part of the literature on guitar chords, which is omitted in the abstract musical-theory of chords for all instruments. For example, in the guitar (like other stringed instruments but unlike the piano ), open-string notes are not fretted and so require less hand-motion.

  9. Power chord - Wikipedia

    en.wikipedia.org/wiki/Power_chord

    The first written instance of a power chord for guitar in the 20th century is to be found in the "Preludes" of Heitor Villa-Lobos, a Brazilian composer of the early twentieth century. Although classical guitar composer Francisco Tárrega used it before him, modern musicians use Villa-Lobos's version to this day. Power chords' use in rock music ...