REVIEW 1 cited by
Unimodal probability distributions for deep ordinal classification
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Unimodal probability distributions for deep ordinal classification
read the original abstract
Probability distributions produced by the cross-entropy loss for ordinal classification problems can possess undesired properties. We propose a straightforward technique to constrain discrete ordinal probability distributions to be unimodal via the use of the Poisson and binomial probability distributions. We evaluate this approach in the context of deep learning on two large ordinal image datasets, obtaining promising results.
Forward citations
Cited by 1 Pith paper
-
Demographic Metadata as Construct-Irrelevant Noise in DistilBERT-Based Automated Essay Scoring
Naive concatenation of demographic metadata to text input in a DistilBERT AES model reduces QWK from 0.727 to 0.656, raises validation loss, and lowers score parity instances from 15 to 12 on the ASAP 2.0 dataset via ...
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.