High--Dimensional Brain in a High-Dimensional World: Blessing of Dimensionality
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Abstract (arXiv)
High-dimensional data and high-dimensional representations of reality are inherent features of modern Artificial Intelligence systems and applications of machine learning. The well-known phenomenon of the "curse of dimensionality" states: many problems become exponentially difficult in high dimensions. Recently, the other side of the coin, the "blessing of dimensionality", has attracted much attention. It turns out that generic high-dimensional datasets exhibit fairly simple geometric properties. Thus, there is a fundamental tradeoff between complexity and simplicity in high dimensional spaces. Here we present a brief explanatory review of recent ideas, results and hypotheses about the blessing of dimensionality and related simplifying effects relevant to machine learning and neuroscience.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Jan 2020.
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Citing papers per year: 2021: 2; 2022: 2; 2023: 0; 2024: 1; 2025: 0; 2026: 1.
6 papers.
- 2021High-dimensional separability for one- and few-shot learningcited by 11
- 2021Demystification of Few-shot and One-shot Learningcited by 10
- 2022Improving VAE-based Representation Learningcited by 6
- 2024A Mathematical Framework for the Problem of Security for Cognition in Neurotechnologycited by 1
- 2022Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systemscited by 0
- 2026The Crowded Embedding Space: A Mean-Field Mechanism for Emergent Marginalization in Retrieval-Augmented Agentscited by 0
- 2026The Crowded Embedding Space: A Mean-Field Mechanism for Emergent Marginalization in Retrieval-Augmented Agents1 Jun 2026
- 2024A Mathematical Framework for the Problem of Security for Cognition in Neurotechnology11 Mar 2024
- 2022Mutual Information Learned Regressor: an Information-theoretic Viewpoint of Training Regression Systems23 Nov 2022
- 2022Improving VAE-based Representation Learning28 May 2022
- 2021High-dimensional separability for one- and few-shot learning28 Jun 2021
- 2021Demystification of Few-shot and One-shot Learning25 Apr 2021