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..
Posterior Re-calibration for Imbalanced Datasets
Authors: Junjiao Tian, Yen-Cheng Liu, Nathaniel Glaser, Yen-Chang Hsu, Zsolt Kira
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results on six different datasets and five different architectures show state of art accuracy, including on large-scale imbalanced datasets such as i Naturalist for classification and Synthia for semantic segmentation. |
| Researcher Affiliation | Academia | Junjiao Tian Georgia Institute of Technology EMAIL Yen-Cheng Liu Georgia Institute of Technology EMAIL Nathaniel Glaser Georgia Institute of Technology EMAIL Yen-Chang Hsu Georgia Institute of Technology EMAIL Zsolt Kira Georgia Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1: UNO-IC: multi-modal fusion algorithm for prior and likelihood shift |
| Open Source Code | Yes | Please see https://github.com/GT-RIPL/UNO-IC.git for implementation. |
| Open Datasets | Yes | Cifar10, Cifar100, i Naturalist18 [21], Synthia [23] are listed in Table 1, and [21] and [23] are formal citations to the dataset papers. |
| Dataset Splits | Yes | The hyperparameter λ is searched on the validation set. We randomly split the validation set into three subsets and use 3-fold cross validation for tuning and testing our IC method. The hyperparameter λ is tuned on a validation split and the algorithm is tested on a test set. |
| Hardware Specification | No | The paper does not provide specific details on hardware specifications (e.g., GPU/CPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper does not specify software dependencies (e.g., library names with version numbers like PyTorch 1.9, TensorFlow 2.x) needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions tuning the hyperparameter λ and provides some values (e.g., "IC λ = 0.4", "UNO-IC λ = 0.4"). However, it does not provide specific values for common experimental setup details like learning rate, batch size, number of epochs, or optimizer settings for the neural network training. |