ArticleProceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference)2020
SAMPLING OF SURFACES AND LEARNING FUNCTIONS IN HIGH DIMENSIONS.
Article in Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing. ICASSP (Conference), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed.
- DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction.Nature methods · 2024Article
- Integrated Protocol of Protein Structure Modeling for Cryo-EM with Deep Learning and Structure Prediction.bioRxiv : the preprint server for biology · 2023Article
- CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning.Nature methods · 2023Article
- Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks.SIAM journal on imaging sciences · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The efficient representation of data in high-dimensional spaces is a key problem in several machine learning tasks. To capture the non-linear structure of the data, we model the data as points living on a smooth surface. We model the surface as the zero level-set of a bandlimited function. We show that this representation allows a non-linear lifting of the surface model, which will map the points to a low-dimensional subspace. This mapping between surfaces and the well-understood subspace model allows us to introduce novel algorithms (a) to recover the surface from few of its samples and (b) to learn a multidimensional bandlimited function from training data. The utility of these algorithms is introduced in practical applications including image denoising.
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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.