Evidence map›Paper›PMID 42633407›Full record

ArticleProceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management2025

LatentExplainer: Explaining Latent Representations in Deep Generative Models with Multimodal Large Language Models.

Mengdan Zhu, Raasikh Kanjiani, Jiahui Lu, Andrew Choi, Qirui Ye, Liang Zhao

Abstract read
In one paragraph

Article in Proceedings of the ... ACM International Conference on Information & Knowledge Management. ACM International Conference on Information and Knowledge Management, 2025. 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

6 authors.

Mengdan ZhuEmory University, Department of Computer Science, Atlanta, GA, USA.
Raasikh KanjianiEmory University, Department of Computer Science, Atlanta, GA, USA.
Jiahui LuUniversity College London, Department of Computer Science, London, United Kingdom.
Andrew ChoiEmory University, Department of Computer Science, Atlanta, GA, USA.
Qirui YeEmory University, Department of Computer Science, Atlanta, GA, USA.
Liang ZhaoEmory University, Department of Computer Science, Atlanta, GA, USA.

Funding

Explainable and Ethical AI for Studying Alzheimer's DiseaseR01AG089806 · NIA · EMORY UNIVERSITY · PI JAMES J LAH, Deqiang Qiu · 2024 to 2026
$2.1M
SCH: Visual explanation-guided learning for human-AI collaborative abdominal cancer diagnostic imagingR01CA297856 · NCI · EMORY UNIVERSITY · PI Liang Zhao · 2024 to 2026
$872k
NCI NIH HHS R01 CA297856NIA NIH HHS R01 AG089806
6 · The paper itself

Abstract

Deep generative models like VAEs and diffusion models have advanced various generation tasks by leveraging latent variables to learn data distributions and generate high-quality samples. Despite the field of explainable AI making strides in interpreting machine learning models, understanding latent variables in generative models remains challenging. This paper introduces

Indexed as

Deep Generative ModelsExplainable AILatent RepresentationsMultimodal Large Language Models

Identifiers

PMID42633407
PMCPMC13499445

What OpenQuestion holds

Textmetadata
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.