Evidence map›Paper›PMID 38464961›Full record

ArticleProceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence2023

Robustness to Spurious Correlations Improves Semantic Out-of-Distribution Detection.

Lily H Zhang, Rajesh Ranganath

Abstract read
In one paragraph

Article in Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

2 authors.

Lily H ZhangCenter for Data Science, New York University.
Rajesh RanganathCenter for Data Science, New York University.

Funding

Deep probabilistic predictive models for stroke and coronary heart diseaseR01HL148248 · NHLBI · NEW YORK UNIVERSITY · PI RANGANATH, RAJESH · 2019 to 2023
$3.3M
NHLBI NIH HHS R01 HL148248
6 · The paper itself

Abstract

Methods which utilize the outputs or feature representations of predictive models have emerged as promising approaches for out-of-distribution (ood) detection of image inputs. However, these methods struggle to detect ood inputs that share nuisance values (e.g. background) with in-distribution inputs. The detection of shared-nuisance out-of-distribution (sn-ood) inputs is particularly relevant in real-world applications, as anomalies and in-distribution inputs tend to be captured in the same settings during deployment. In this work, we provide a possible explanation for sn-ood detection failures and propose

Identifiers

PMID38464961
PMCPMC10923583

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.