How to post notebooks?
It turns out that blog-posting Jupyter notebooks with Jekyll (and the Beautiful Jekyll theme) is enjoyably simple. One just needs to follow the instructions below.
[Read More]
This is becoming my personal website. I may post on machine learning and other things. Everything is work in progress.
-
-
Denoising diffusion models
A brief introduction to generative diffusion modeling is provided in this blog post. In particular, the discussion focuses on the denoising diffusion probabilistic model (DDPM) [Sohl-Dickstein et al., 2015; Ho et al., 2020]. The relation to other generative modeling approaches such as energy-based models (EBMs), variational autoencoders (VAEs) or normalizing... [Read More] -
Variational autoencoder
This blog post contains a tutorial on the variational autoencoder (VAE). Since its introduction by [Kingma and Welling, 2014, Rezende et al., 2014], the framework has received much attention for its generative modeling and representation learning capabilities. Recent reviews are given in [Doersch, 2016, Kingma and Welling, 2019, Wei et... [Read More] -
GP models
An introduction to Gaussian process modeling is given in this short note. Gaussian processes for example enable regression with built-in uncertainty quantification. [Read More] -
Physics-informed neural nets
The idea of using neural networks (NNs) for solving partial differential equations (PDEs) has been around for some time [Dissanayake and Phan-Thien, 1994; Lagaris et al., 1998]. Following the advances in fields like computer vision and natural language processing, NNs have recently enjoyed growing interest in computational science and engineering,... [Read More] -
Attention mechanism
This blog post gives a brief introduction to attention in deep neural nets. Attention establishes a mechanism that allows a model to make predictions based on selectively attending to different items of an input sequence. It can be employed as a pretty generic modeling layer for problems with a sequential... [Read More] -
Contrastive learning
This blog post provides an overly brief introduction to contrastive representation learning. In particular, we restrict the discussion to the contrastive loss [Hadsell et al., 2006] and the triplet loss [Schroff et al., 2015]. A more comprehensive review can be found in [Le-Khac et al., 2020]. A common field of... [Read More] -
Adversarial ML
The existence of adversarial examples for neural networks has been first observed in the context of image classification [Szegedy et al., 2014]. There are many great review papers on adversarial attacks and corresponding defenses. For example, the following publications are open access: [Ren et al., 2020; Khamaiseh et al., 2022;... [Read More] -
Welcome!
This is my attempt at starting a blog. It will be mainly concerned with machine learning, neural networks, computer vision and generative AI. Further topics could include music, biking or parenting. Watch out, you may encounter irony! [Read More]