arXiv:2608. 20326v1 Announce Type: cross Abstract: Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
Deep neural networks are often overconfident, assigning high confidence even to incorrect predictions. Consequently, users lack a reliable signal for deciding when a prediction can be trusted.
arXiv:2608. 09768v1 Announce Type: new Abstract: A prediction that is both confident and wrong is a critical reliability failure because it can bypass abstention and human review precisely when the model is mistaken.
The paper introduces pseudo‑ensembles for music audio‑language models to enable abstention when the model is uncertain. By perturbing inputs—such as shuffling answer order, corrupting audio, or swapping option labels—multiple predictive distributions are generated from a single pretrained model, allowing the use of ensemble‑based uncertainty metrics like entropy, expected entropy, and mutual information. Experiments on TinyMU with MuChoMusic show that averaging over four answer orderings improves accuracy from 55.7% to 59.2% and reduces the error‑retention curve area from 0.293 to 0.261, all with only a few extra forward passes and no retraining.
arXiv:2610.27443v1 Announce Type: new Abstract: Set-valued classification (SVC) offers a practical approach to communicating uncertainty while maintaining accuracy by predicting variable-sized sets p...
arXiv:2509.24988v2 Announce Type: replace-cross Abstract: Generating accurate and calibrated confidence estimates is critical for deploying LLMs in high-stakes or user-facing applications, and remain...
arXiv:2606. 28654v1 Announce Type: cross Abstract: Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likelihoods.