Fwd: Network seminar / Big Data Embedding metdhos Seminar 27th April

[Liubov]

Dear all,

The coming week we will have a very interesting seminar by Dimitrii Kobak on various embedding methods on 27th April at 4pm CET.

Register for the seminar here:
https://forms.gle/s8NbxYwVXDLKe53N7

What? Seminar by Dimitri Kobak (Germany).

Title: Contrastive and neighbor embedding methods for data visualization

Abstract: In recent years, neighbor embedding methods like t-SNE and
UMAP have become widely used across several application fields, in
particular in single-cell biology. They are also widely used for
visualizing large collections of documents and/or images used to train
modern deep learning architectures such as large language models or
diffusion models. Given this academic and public attention, it is very
important to understand possibilities, shortcomings, and trade-offs of
neighbor embedding methods. I am going to present our recent work on
the attraction-repulsion spectrum of neighbor embeddings and the
involved trade-offs. I am also going to explain how neighbor
embeddings are related to contrastive learning, a popular framework
for self-supervised learning of image data. This will lead to our
recent work on contrastive visualizations of image datasets. In the
second part of the talk, I will present our ongoing work on
visualization of scientific literature, in particular biomedical
research papers from the PubMed library.

Short bio: Dmitry Kobak is a research scientist and a group leader in
the Berens lab at Tübingen University, Germany. He is interested in
unsupervised and self-supervised learning, in particular contrastive
learning, manifold learning, and dimensionality reduction for 2D
visualization of biological datasets.

More information about Dimitrii and some work he made publichttps://twitter.com/hippopedoid
Some of his publications:
https://www.biorxiv.org/content/10.1101/2023.04.10.536208

On behalf of the organisation team of the seminar,

Liubov, Marc et al.