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..
Improving Deep Regression with Ordinal Entropy
Authors: Shihao Zhang, Linlin Yang, Michael Bi Mi, Xiaoxu Zheng, Angela Yao
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on synthetic and real-world regression tasks demonstrate the importance and benefits of increasing entropy for regression. |
| Researcher Affiliation | Collaboration | 1National University of Singapore, Singapore, 2Huawei International Pte Ltd, Singapore |
| Pseudocode | No | No pseudocode or algorithm block was found in the paper. |
| Open Source Code | Yes | Code can be found here: https://github.com/needylove/Ordinal Entropy |
| Open Datasets | Yes | NYU-Depth-v2 (Silberman et al., 2012) provides indoor images with the corresponding depth maps at a pixel resolution 640 480. |
| Dataset Splits | No | No explicit specification of training/validation/test splits with percentages or counts was provided. For real-world datasets, it mentions 'train/test split given used by previous works (Bhat et al., 2021; Yuan et al., 2022)' and for synthetic data, it only specifies '1k data as the training set and test on the testing set with 100k samples' without detailing a validation split. |
| Hardware Specification | No | No specific hardware details such as GPU models (e.g., NVIDIA A100), CPU models, or detailed computer specifications were provided for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., 'Python 3.8, PyTorch 1.9') were provided for replicating the experiment. |
| Experiment Setup | Yes | We use the trade-off parameters λd, λt the same value of 0.001, 1, 10, 1, for operator learning, depth estimation, crowd counting and age estimation, respectively. |