Posts by Tags

deep generative models

Denoising diffusion probabilistic models

11 minute read

Published:

The majority of deep generative models proposed in the last few years have broadly fallen under three categories—generative adversarial networks (GANs), variational autoencoders (VAEs), and normalizing flows. There are few others, such as autoregressive models and those based on transformers, but they are much slower to sample. As a result, they are not widely used, particularly when the distribution being modeled is very high-dimensional.

evidence lower bound

Denoising diffusion probabilistic models

11 minute read

Published:

The majority of deep generative models proposed in the last few years have broadly fallen under three categories—generative adversarial networks (GANs), variational autoencoders (VAEs), and normalizing flows. There are few others, such as autoregressive models and those based on transformers, but they are much slower to sample. As a result, they are not widely used, particularly when the distribution being modeled is very high-dimensional.

generalized least squares

Least Squares

less than 1 minute read

Published:

Here’s a little piece on the different pictures of linear least squares I wrote for towardsdatascience. All the code used to generate the plots can be found in this github repo.

google summer of code

GSoC

less than 1 minute read

Published:

Back in 2016, I took part in a large open-source program called Google Summer of Code or GSoC. Specifically, I worked with an organization called BRL-CAD which specialised in a computer-aided design (CAD) software. Here is the official project page from GSoC archives.

linear regression

Least Squares

less than 1 minute read

Published:

Here’s a little piece on the different pictures of linear least squares I wrote for towardsdatascience. All the code used to generate the plots can be found in this github repo.

maximum likelihood estimation

Least Squares

less than 1 minute read

Published:

Here’s a little piece on the different pictures of linear least squares I wrote for towardsdatascience. All the code used to generate the plots can be found in this github repo.

open source

GSoC

less than 1 minute read

Published:

Back in 2016, I took part in a large open-source program called Google Summer of Code or GSoC. Specifically, I worked with an organization called BRL-CAD which specialised in a computer-aided design (CAD) software. Here is the official project page from GSoC archives.

ordinary least squares

Least Squares

less than 1 minute read

Published:

Here’s a little piece on the different pictures of linear least squares I wrote for towardsdatascience. All the code used to generate the plots can be found in this github repo.

ray tracing

GSoC

less than 1 minute read

Published:

Back in 2016, I took part in a large open-source program called Google Summer of Code or GSoC. Specifically, I worked with an organization called BRL-CAD which specialised in a computer-aided design (CAD) software. Here is the official project page from GSoC archives.

stochastic diffusion models

Denoising diffusion probabilistic models

11 minute read

Published:

The majority of deep generative models proposed in the last few years have broadly fallen under three categories—generative adversarial networks (GANs), variational autoencoders (VAEs), and normalizing flows. There are few others, such as autoregressive models and those based on transformers, but they are much slower to sample. As a result, they are not widely used, particularly when the distribution being modeled is very high-dimensional.