Evidence map›Paper›PMID 39711614›Full record

ArticlePhysical review research

Holographic-(V)AE: An end-to-end SO(3)-equivariant (variational) autoencoder in Fourier space.

Gian Marco Visani, Michael N Pun, Arman Angaji, Armita Nourmohammad

Abstract read
In one paragraph

Article in Physical review research. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Gian Marco VisaniPaul G. Allen School of Computer Science and Engineering, University of Washington, 85 E Stevens Way NE, Seattle, Washington 98195, USA.ORCID 0000-0003-0888-0922
Michael N PunDepartment of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, Washington 98195, USA.ORCID 0000-0002-0996-6798
Arman AngajiInstitute for Biological Physics, University of Cologne, Zülpicher Str. 77, 50937 Cologne, Germany.ORCID 0000-0001-7074-9429
Armita NourmohammadDepartment of Physics, University of Washington, 3910 15th Avenue Northeast, Seattle, Washington 98195, USA.ORCID 0000-0002-6245-3553

Funding

Learning a molecular shape space for the adaptive immune systemR35GM142795 · NIGMS · UNIVERSITY OF WASHINGTON · PI NOURMOHAMMAD, ARMITA · 2021 to 2025
$1.8M
NIGMS NIH HHS R35 GM142795
6 · The paper itself

Abstract

Group-equivariant neural networks have emerged as an efficient approach to model complex data, using generalized convolutions that respect the relevant symmetries of a system. These techniques have made advances in both the supervised learning tasks for classification and regression, and the unsupervised tasks to generate new data. However, little work has been done in leveraging the symmetry-aware expressive representations that could be extracted from these approaches. Here, we present

Identifiers

PMID39711614
PMCPMC11661850

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.