https://joseph-nagel.github.io/This is a blog on machine learning, neural networks, computer vision, generative AI and Bayesian inference. 2026-10-28T13:17:20+01:00 Joseph Nagel https://joseph-nagel.github.io/ Jekyll © 2026 Joseph Nagel /assets/img/favicons/favicon.ico /assets/img/favicons/favicon-96x96.png 2026-10-27T00:00:00+01:00 2026-10-27T00:00:00+01:00 https://joseph-nagel.github.io/posts/jepa/ Joseph Nagel

We are going to take a short look at the joint-embedding predictive architecture (JEPA). JEPA is a form of self-supervised learning (SSL) whose basic idea is the prediction between latent space representations of related objects [LeCun, 2022]. In particular, the representation is required to be predictable from unobserved parts of the input, giving rise to a latent space that ignores unpredicta...

2026-10-19T00:00:00+02:00 2026-10-19T00:00:00+02:00 https://joseph-nagel.github.io/posts/ssl/ Joseph Nagel

In this blog post, we will briefly discuss image-based self-supervised learning (SSL). This allows one to learn a useful data representation based on automatically generating a supervisory signal from unlabeled data alone. In particular, we focus on the masked autoencoder (MAE) [He et al., 2021] and self-distillation with no labels (DINO) [Caron et al., 2021], which are two widely used schemes ...

2026-09-10T00:00:00+02:00 2026-09-10T00:00:00+02:00 https://joseph-nagel.github.io/posts/pre-commit/ Joseph Nagel

This post gathers a few short notes on Git pre-commit hooks. Before changes are committed to a Git repository, such hooks may automatically check or format the new code. In this way, one can enforce coding standards and best practices, which helps to maintain code quality and consistency throughout the development process. It has to be noted that pre-commit hooks should be used in addition to ...

2026-08-30T00:00:00+02:00 2026-08-30T00:00:00+02:00 https://joseph-nagel.github.io/posts/conformal-prediction/ Joseph Nagel

This blog post contains a brief introduction to conformal prediction (CP), a statistical framework for uncertainty quantification in machine learning [Vovk et al., 2022]. The uncertainty of model predictions is quantified through prediction sets or intervals that contain the true outcome with a high probability. CP has a number of compelling advantages: finite-sample guarantees: does not nee...

2026-07-01T00:00:00+02:00 2026-10-28T13:13:38+01:00 https://joseph-nagel.github.io/posts/pfn/ Joseph Nagel

My whole academic experience was focused on tabular data in one way or another, whereas I have spent most of my career in industry working with computer vision foundation models. The recent implementations of tabular foundation models (TFMs) therefore sparked quite some curiosity and a little astonishment on my end. This blog post is simply a brief overview of current TFMs that are based on pr...