Evidence map›Paper›PMID 34557339›Full record

ArticleIEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing2020

LEARNING GENERAL TRANSFORMATIONS OF DATA FOR OUT-OF-SAMPLE EXTENSIONS.

Matthew Amodio, David van Dijk, Guy Wolf, Smita Krishnaswamy

Abstract read
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In one paragraph

Article in IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Matthew AmodioYale University, Dept. of Computer Science, New Haven, CT, USA.
David van DijkYale University, Dept. of Cardio. Medicine, New Haven, CT, USA.
Guy WolfUniversité de Montréal, Dept. of Math. & Stat.; Mila - Quebec AI Institute, Montréal, QC, Canada.
Smita KrishnaswamyYale University, Dept. of Computer Science, New Haven, CT, USA.

Funding

Deciphering Genetic and Epigenetic Regulatory Logic of Germ Layer Differentiation with Manifold LearningR01GM130847 · NIGMS · YALE UNIVERSITY · PI KRISHNASWAMY, SMITA · 2019 to 2023
$2.0M
Finding emergent structure in multi-sample biological data with the dual geometry of cells and features R01GM135929 · NIGMS · MICHIGAN STATE UNIVERSITY · PI BROWN, EDWARD · 2019 to 2022
$1.4M
NIGMS NIH HHS R01 GM130847NIGMS NIH HHS R01 GM135929
6 · The paper itself

Abstract

While generative models such as GANs have been successful at mapping from noise to specific distributions of data, or more generally from one distribution of data to another, they cannot isolate the transformation that is occurring and apply it to a new distribution not seen in training. Thus, they memorize the domain of the transformation, and cannot generalize the transformation

Identifiers

PMID34557339
PMCPMC8456777

What OpenQuestion holds

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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.