# t-SNE Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/t-sne
> Markdown URL: https://aitinkerers.org/technologies/t-sne.md
> Technology record last updated: 2026-09-18T15:13:52Z
> Generated: 2026-09-23T02:27:24Z

A nonlinear dimensionality reduction algorithm that visualizes high-dimensional data by clustering similar points in 2D or 3D space.

Developed by Laurens van der Maaten and Geoffrey Hinton in 2008, t-SNE (t-distributed Stochastic Neighbor Embedding) excels at preserving local structures within complex datasets. The algorithm converts Euclidean distances into conditional probabilities: it uses Gaussian distributions in high-dimensional space and a Student t-distribution in the low-dimensional map to prevent the crowding problem. This specific math makes it the industry standard for visualizing single-cell RNA sequencing (scRNA-seq) results and deep learning feature maps. While linear methods like PCA (Principal Component Analysis) focus on global variance, t-SNE reveals the intricate, non-linear clusters essential for exploratory data analysis.

- Official technology site: https://lvdmaaten.github.io/tsne/
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Contrasting Language Omics Pretraining](https://lausanne.aitinkerers.org/talks/rsvp_ZjMIQM_LY9M)

CLOP is an adaptation of OpenAI's CLIP but for Omics - in this demo, genomics. The model is trained on fasta, bed and gff files (representing genomes of different species and their annotations) to learn meaningful representations for further retrieval, classification and generation purposes. The model embeds DNA sequences according to species and biotype (e.g. exon, long non coding RNA, pseudogene, etc.)

- Event context: AI Tinkerers Lausanne June 2025 Meetup — 2025-06-16 — Lausanne
- Public talk page: https://lausanne.aitinkerers.org/talks/rsvp_ZjMIQM_LY9M

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