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Variational autoencoders (VAEs) are specific types of autoencoders that learn to encode input data into a latent space involving probability, "organizing the space". This allows for the generation of new data samples that are consistent and useful. Unlike traditional autoencoders, which aim to map inputs to a deterministic latent space, VAEs ...
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AI • Machine Learning • Tech on Instagram: "The reparameterization trick is a clever technique used in Variational Autoencoders (VAEs) to make the training process possible using standard gradient-based optimization. Normally, VAEs learn to generate data by first learning a distribution (like a Gaussian) from which to sample, but sampling from this distribution means we can’t backpropagate the loss to update the model (we can’t differentiate a sampling process!). The reparameterization trick sol
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Seren on Instagram: "Started with a simple voice recording, ran it through RAVE* (Realtime Audio Variational autoEncoder, an AI model that transforms audio into different formats) to turn it into percussion, then layered it under the original recording to build this track ✨️ Exploring how AI can inspire the creative process is truly exciting. Reference: Caillon, A., Defossez, A., & Esling, P. (2021). RAVE: A variational autoencoder for fast and high-quality neural audio synthesis. @ircam_paris #
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Seren on Instagram: "Started with a simple voice recording, ran it through RAVE* (Realtime Audio Variational autoEncoder, an AI model that transforms audio into different formats) to turn it into percussion, then layered it under the original recording to build this track ✨️ Exploring how AI can inspire the creative process is truly exciting. Reference: Caillon, A., Defossez, A., & Esling, P. (2021). RAVE: A variational autoencoder for fast and high-quality neural audio synthesis. @ircam_paris #
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AI • Machine Learning • Tech on Instagram: "The reparameterization trick is a clever technique used in Variational Autoencoders (VAEs) to make the training process possible using standard gradient-based optimization. Normally, VAEs learn to generate data by first learning a distribution (like a Gaussian) from which to sample, but sampling from this distribution means we can’t backpropagate the loss to update the model (we can’t differentiate a sampling process!). The reparameterization trick sol
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Artificial Intelligence | AI on Instagram: "A Variational Autoencoder (VAE) is a type of neural network (specifically an autoencoder) used for unsupervised learning that uses probability to generate images. Like any other autoencoder, it learns to encode data into a compressed latent space and then decode it back into its original form. The training process involves two main components: an encoder, which maps input data to a probabilistic distribution in the latent space, and a decoder, which re
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