I like that someone did a followup to 3blue1brown video on near-orthogonality:

Beyond Orthogonality: How Language Models Pack Billions of Concepts into 12,000 Dimensions

https://nickyoder.com/johnson-lindenstrauss/

> This research suggests that current embedding dimensions (1,000-20,000) provide more than adequate capacity for representing human knowledge and reasoning. The challenge lies not in the capacity of these spaces but in learning the optimal arrangement of concepts within them.

#highdimensional #math

🧠 New comprehensive review on #LowDimensional #embeddings of #HighDimensional data. Discusses how #dimensionalityreduction helps visualizing, exploring, and #modeling #ComplexSystems. From #PCA to #tSNE, #UMAP & #NeuralNetworks: Excellent overview paper👌

🌍 https://arxiv.org/abs/2508.15929

#CompNeuro #MachineLearning #DataVisualization

I am looking for a new position in computational biology/bioinformatics preferably in #academia in the #USA.

I am an experienced Computational Biologist with eight years of post-PhD experience in applying computational methods across diverse domains, from molecular modeling to #highdimensional and #machinelearning based single-cell flow/mass cytometry and serum proteomics data analysis.

Any leads would be helpful.

#computationalbiology #bioinformatics #jobsearch #lookingforwork

Looks like advances in neural computing theory might have analog computing back with a vengueance. 🤯

#reservoircomputing #analog #computing #highdimensional #computingspace

https://www.nature.com/articles/srep22381

Does anyone know of a research project which explored structuring oppositions in #highdimensional spaces constructed from fictional narratives? #digitalhumanities

I have come across this only from an approach in art history, where the artworks represented in the vector space were distributed in clusters, and therefore represented oppositions such as naturalism vs. impressionism.

See
https://digital-narratives.versae.es/#