Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Data-Driven Measure of Relative Uncertainty for Misclassification Detection
Authors: Eduardo Dadalto Câmara Gomes, Marco Romanelli, Georg Pichler, Pablo Piantanida
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate empirical improvements over multiple image classification tasks, outperforming state-of-the-art misclassification detection methods. |
| Researcher Affiliation | Academia | Eduardo Dadalto Laboratoire des signaux et systèmes (L2S) Université Paris-Saclay CNRS Centrale Supélec Gif-sur-Yvette, France; Marco Romanelli New York University New York, NY, USA; Georg Pichler Institute of Telecommunications TU Wien 1040 Vienna, Austria; Pablo Piantanida International Laboratory on Learning Systems (ILLS) Quebec AI Institute (MILA) CNRS Centrale Supélec Université Paris-Saclay Montreal, Canada |
| Pseudocode | Yes | Algorithm 1 Offline relative uncertainty matrix computation. |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is available, nor does it provide a link to a repository or supplementary materials for code access. |
| Open Datasets | Yes | Table 1 showcases the misclassification detection performance... trained on different datasets (CIFAR-10, CIFAR-100 (Krizhevsky, 2009)). |
| Dataset Splits | Yes | We split the test set into two sets: one portion for tuning the detector (held out validation set) and the other for evaluating it. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions general software components like |
| Experiment Setup | Yes | For our method, we tuned the best lambda parameter (λ), T, and ϵ. |