ArticleNeural computation2021
Parametric UMAP Embeddings for Representation and Semisupervised Learning.
Article in Neural computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
106 citing papers in PubMed.
- Single-cell gene regulatory networks characterize colonic stem and immune cell homeostasis iniScience · 2026Article
- Ternary Neurexin-T178-PTPR complexes represent a pre-synaptic core-module of neuronal synapse organization.Nature communications · 2026Article
- A unified developmental perspective on common cavity and cochlear aplasia deformities: morphological subtypes and spectrum analysis based on 3D shape radiomics.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Truthful visualizations for mass spectrometry imaging enable high-spatial-resolution interactiveScience advances · 2026Article
- Review
- Dynamic Landscape Analysis of cell fate decisions provides predictive models of neural development from single-cell data.PLoS biology · 2026Article
- Unlocking the Chemical Space for Rechargeable Batteries with a Generative Solvent Design System.ACS nano · 2026Article
- Molecular arms race classifier for decrypting venom peptide and ion channel interactions.Digital discovery · 2026Article
- An RNA Language Model trained on sequence alone reveals the structural logic of Internal Ribosome Entry Sites.bioRxiv : the preprint server for biology · 2026Article
- Generalization of long-range machine learning potentials in complex chemical spaces.Digital discovery · 2026Article
- Towards precision oncology: unsupervised manifold learning for spatial molecular profiling in cancer tissues.BMC bioinformatics · 2026Article
- Nonlinear dimensionality reduction and Bayesian optimization for accelerating design of materials.Scientific reports · 2026Article
- CD38⁺ endothelial remodeling marks spatially patterned vasculopathy in rapidly advancing periodontitis and peri-implantitis.Nature communications · 2026Article
- Zinc accumulation-induced integrated stress response triggers β-cell identity loss.Cell research · 2026Article
- MIAAIM: Multi-omics image integration with dimensional reduction for tissue state mapping.PLoS computational biology · 2026Article
- Phenotyping of Patients with Age-Related Macular Degeneration Using Artificial Intelligence-Driven Biomarker Patterns in OCT.Ophthalmology science · 2026Article
- Target-enhanced double-pulse LIBS coupled with feature-fused CNN for mechanistic and interpretable coffee origin authentication.Food chemistry: X · 2026Article
- Time-resolved functional genomics using deep learning reveals global hierarchical control of autophagy.Nature cell biology · 2026Article
- Machine Learning Exploration of Food-Derived Chemical Space for Potential Nutritional Metabolic Regulators Targeting Dipeptidyl Peptidase-4.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Integrative profiling of condensation-prone RNAs during early development.Cell genomics · 2026Article
46 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
UMAP is a nonparametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured data. The UMAP algorithm consists of two steps: (1) computing a graphical representation of a data set (fuzzy simplicial complex) and (2) through stochastic gradient descent, optimizing a low-dimensional embedding of the graph. Here, we extend the second step of UMAP to a parametric optimization over neural network weights, learning a parametric relationship between data and embedding. We first demonstrate that parametric UMAP performs comparably to its nonparametric counterpart while conferring the benefit of a learned parametric mapping (e.g., fast online embeddings for new data). We then explore UMAP as a regularization, constraining the latent distribution of autoencoders, parametrically varying global structure preservation, and improving classifier accuracy for semisupervised learning by capturing structure in unlabeled data.1.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.