Graphical models | Neural network architectures | Dimension reduction | Bayesian statistics
In machine learning, a variational autoencoder (VAE), is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling, belonging to the families of probabilistic graphical models and variational Bayesian methods. Variational autoencoders are often associated with the autoencoder model because of its architectural affinity, but with significant differences in the goal and mathematical formulation. Variational autoencoders are probabilistic generative models that require neural networks as only a part of their overall structure, as e.g. in VQ-VAE. The neural network components are typically referred to as the encoder and decoder for the first and second component respectively. The first neural network maps the input variable to a latent space that corresponds to the parameters of a variational distribution. In this way, the encoder can produce multiple different samples that all come from the same distribution. The decoder has the opposite function, which is to map from the latent space to the input space, in order to produce or generate data points. Both networks are typically trained together with the usage of the reparameterization trick, although the variance of the noise model can be learned separately. Although this type of model was initially designed for unsupervised learning, its effectiveness has been proven for semi-supervised learning and supervised learning. (Wikipedia).
Free ebook http://tinyurl.com/EngMathYT I show how to solve differential equations by applying the method of variation of parameters for those wanting to review their understanding.
From playlist Differential equations
Differential Equations | Variation of Parameters.
We derive the general form for a solution to a differential equation using variation of parameters. http://www.michael-penn.net
From playlist Differential Equations
C28 Variation of parameters Part 1
We have already seen variation of parameters in action, but here we expand the method for use in second-order linear DE's, even with non-constant coefficients.
From playlist Differential Equations
C07 Homogeneous linear differential equations with constant coefficients
An explanation of the method that will be used to solve for higher-order, linear, homogeneous ODE's with constant coefficients. Using the auxiliary equation and its roots.
From playlist Differential Equations
C29 Variation of parameters Part 2
I continue with an explanation of the method of variation of parameters.
From playlist Differential Equations
A16 The method of variation of parameters
Starting the derivation for the equation that is used to find the particular solution of a set of differential equations by means of the variation of parameters.
From playlist A Second Course in Differential Equations
C34 Expanding this method to higher order linear differential equations
I this video I expand the method of the variation of parameters to higher-order (higher than two), linear ODE's.
From playlist Differential Equations
z-Transform Analysis of LTI Systems
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Introduction to analysis of systems described by linear constant coefficient difference equations using the z-transform. Definition of the system fu
From playlist The z-Transform
Variational Autoencoders - EXPLAINED!
In this video, we are going to talk about Generative Modeling with Variational Autoencoders (VAEs). The explanation is going to be simple to understand without a math (or even much tech) background. However, I also introduce more technical concepts for you nerds out there while comparing V
From playlist Variational AutoEncoders
Generative machine learning models have the potential to allow us to move beyond screening to true materials discovery. Generative adversarial networks (GANs) are one powerful tool and variational autoencoders (VAEs) are another. This video descrbies autoencoders, latent space, reparameter
From playlist Materials Informatics
Variation of Parameters for Systems of Differential Equations
This is the second part of the variation of parameters-extravaganza! In this video, I show you how to use the same method in the last video to solve inhomogeneous systems of differential equations. Witness how linear algebra makes this method so elegant!
From playlist Differential equations
Understand Vector-Quantized Variational Autoencoder (VQ-VAE) for Image Generation #stablediffusion
Vector-Quantized Variational Autoencoder (VQ-VAE) for Image Generation explained in Detail. The main component of DALL-E of 2020. On the road to DIFFUSION for text-to-video. Generative AI. 3 videos before this video: https://youtu.be/onuIy6PC1ic https://youtu.be/BHzU-jq-AcM https://youtu.
From playlist Stable Diffusion / Latent Diffusion models for Text-to-Image AI
Extracting Biologically Relevant Latent Space from Cancer Transcriptomes \w VAEs (algorithm) | AISC
Toronto Deep Learning Series, 7 December 2018 Paper Review: https://www.biorxiv.org/content/biorxiv/early/2017/08/11/174474.full.pdf Discussion Lead: Shagun Maheshwari (TKS) Discussion Facilitator: Jane Illarionova, Kayvan Tirdad Host: Aviva Digital Garage Date: January 7th, 2018 Extr
From playlist Machine Learning for Scientific Discovery
The Images in-between | Before Diffusion: Variational Autoencoder VAE explained w/ KL Divergence
After coding VAE in Python (see my vid https://youtu.be/pRKTr8gw2KA), a lot of questions emerged from my viewers. Therefore: Variational AUTOENCODERS (VAE) explained: Theory of VAE. On the road to Diffusion theory for text-to-image and text-to-video. Generative AI. The answers to all yo
From playlist Stable Diffusion / Latent Diffusion models for Text-to-Image AI
What is an Autoencoder? | Two Minute Papers #86
Autoencoders are neural networks that are capable of creating sparse representations of the input data and can therefore be used for image compression. There are denoising autoencoders that after learning these sparse representations, can be presented with noisy images. What is even better
From playlist Introduction to Deep Learning
Lecture 13 | Generative Models
In Lecture 13 we move beyond supervised learning, and discuss generative modeling as a form of unsupervised learning. We cover the autoregressive PixelRNN and PixelCNN models, traditional and variational autoencoders (VAEs), and generative adversarial networks (GANs). Keywords: Generative
From playlist Lecture Collection | Convolutional Neural Networks for Visual Recognition (Spring 2017)
Transformer Neural Net makes music! (JukeboxAI)
JukeboxAI can generate music in the voice of any artist with any style. Please subscribe to keep me alive: https://www.youtube.com/c/CodeEmporium?sub_confirmation=1 SPONSOR Kite is a free AI-powered coding assistant that will help you code faster and smarter. The Kite plugin integrates w
From playlist Deep Learning Research Papers
Data around us, like images and documents, are very high dimensional. Autoencoders can learn a simpler representation of it. This representation can be used in many ways: - fast data transfers across a network - Self driving cars (Semantic Segmentation) - Neural Inpainting: Completing sect
From playlist Algorithms and Concepts
Introduction to Direct Variation, Inverse Variation, and Joint Variation
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Introduction to Direct Variation, Inverse Variation, and Joint Variation
From playlist 3.7 Modeling Using Variation
Denoising and Variational Autoencoders
A video about autoencoders, a very powerful generative model. The video includes: Intro: (0:25) Dimensionality reduction (3:35) Denoising autoencoders (10:50) Variational autoencoders (18:15) Training autoencoders (23:36) Github repo: www.github.com/luisguiserrano/autoencoders Recommende
From playlist Unsupervised Learning