The theory behind Latent Variable Models: formulating a Variational Autoencoder | AI Summer
The theory behind Latent Variable Models: formulating a Variational Autoencoder | AI Summer
Feb 4, 2021
theaisummer.com
AI • Machine Learning • Tech on Instagram: "A variational autoencoder (VAE) is a generative model that learns to represent data not as fixed points, but as probability distributions in a hidden space. Like other neural networks, it uses large matrices with tunable parameters to process the input, but instead of producing a single compressed vector, it outputs a mean and variance that define a distribution the input could come from. During training, the model samples from this distribution and tr
1:02
AI • Machine Learning • Tech on Instagram: "A variational autoencoder (VAE) is a generative model that learns to represent data not as fixed points, but as probability distributions in a hidden space. Like other neural networks, it uses large matrices with tunable parameters to process the input, but instead of producing a single compressed vector, it outputs a mean and variance that define a distribution the input could come from. During training, the model samples from this distribution and tr
15.7K views5 months ago
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Exploring Generative AI Models: Autoencoders, VAEs & Diffusion Models | Aman Chauhan posted on the topic | LinkedIn
Exploring Generative AI Models: Autoencoders, VAEs & Diffusion Models | Aman Chauhan posted on the topic | LinkedIn
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