Variational Auto-Encoders and NVIB

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Related lectures (31)

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Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Deep Generative Models

Covers deep generative models, including LDA, autoencoders, GANs, and DCGANs.

Deep Generative Models

Covers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.

Document Analysis and Topic Modeling

Covers document analysis, topic modeling, and deep generative models, including autoencoders and GANs.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Variational Autoencoders

Covers Variational Autoencoders, a probabilistic approach to autoencoders for data generation and feature representation, with applications in Natural Language Processing.

Deep Generative Models: Part 2

Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.

Topic Models

Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

Topic Models: Latent Dirichlet Allocation

Introduces Latent Dirichlet Allocation for topic modeling in documents, discussing its process, applications, and limitations.