Steffen Czolbe

PhD Fellow in Machine Learning at Copenhagen University

[Steffen Czolbe @SCzolbe]

The new method outperforms baselines from topological change detection and anomaly detection by a large margin and competes with supervised models on one of the datasets.
Code available at https://t.co/R7the0mQzj
5/5

[Steffen Czolbe @SCzolbe]

Can we detect when geometric alignment algorithms make errors? 🔍
We introduce the first unsupervised, learning-based method for detecting changes in image topology, along with a new dataset for evaluation. Accepted to #neurips2021 https://t.co/0Tcn1ZrN3b
1/5

[Steffen Czolbe @SCzolbe]

We introduce the first dataset with annotated topological changes to aid evaluation in this field, which has been relying on qualitative assessment of small sample sizes and proxy-tasks before.
4/5

[Steffen Czolbe @SCzolbe]

A very interesting take at machine learning as "Software 2.0", it's pros and cons compared to traditional "Software 1.0"

https://t.co/ejQQ0eUIDP

[Steffen Czolbe @SCzolbe]

Want to understand the LDDMM diffeomorphic image registration algorithm by @drmfbeg?

I published a simple, fully commented python implementation. The code references equations of the original paper and makes learning simple.

https://t.co/AwWQowAbsk