/explore
|
You are here 
|
blog.mozilla.ai | ||
| | | | |
doomlab.github.io
|
|
| | | | | Shiny Apps Each Shiny application is listed below, along with any relevant publication information. MOTE: Magnitude of the Effect Shiny App GitHub Research Paper This app allows you to calculate many effect sizes and their confidence intervals. We have included mean differences and variance overlap effect sizes, their formulas, easy to copy output in APA style, help videos on how to use our app, and code for R users. Alternatives to Null Hypothesis Significance Testing Shiny App GitHub Research Paper This app provides color visualization of the data from our paper that focuses on alternatives to NHST procedures. | |
| | | | |
reasonabledeviations.com
|
|
| | | | | Academic blog about quantitative finance, programming, maths. | |
| | | | |
freerangestats.info
|
|
| | | | | p-values under the null hypothesis do not necessarily have a uniform distribution. | |
| | | | |
iclr-blogposts.github.io
|
|
| | | Diffusion Models, a new generative model family, have taken the world by storm after the seminal paper by Ho et al. [2020]. While diffusion models are often described as a probabilistic Markov Chains, their underlying principle is based on the decade-old theory of Stochastic Differential Equations (SDE), as found out later by Song et al. [2021]. In this article, we will go back and revisit the 'fundamental ingredients' behind the SDE formulation and show how the idea can be 'shaped' to get to the modern form of Score-based Diffusion Models. We'll start from the very definition of the 'score', how it was used in the context of generative modeling, how we achieve the necessary theoretical guarantees and how the critical design choices were made to finally arri... | ||
