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..
Unimodal Probability Distributions for Deep Ordinal Classification
Authors: Christopher Beckham, Christopher Pal
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate this approach in the context of deep learning on two large ordinal image datasets, obtaining promising results. |
| Researcher Affiliation | Academia | 1Montr eal Institute of Learning Algorithms, Qu ebec, Canada. Correspondence to: Christopher Beckham <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code will be made available here.5 https://github.com/christopher-beckham/deep-unimodalordinal |
| Open Datasets | Yes | Diabetic retinopathy1. ... 1https://www.kaggle.com/c/diabetic-retinopathy-detection/ |
| Dataset Splits | Yes | A validation set is set aside, consisting of 10% of the patients in the training set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Theano, Lasagne, and Keras but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | All experiments utilise an ℓ2 norm of 10 4, ADAM optimiser (Kingma & Ba, 2014) with initial learning rate 10 3, and batch size 128. A manual learning rate schedule is employed where we manually divide the learning rate by 10 when either the validation loss or valid set QWK plateaus (whichever plateaus last) down to a minimum of 10 4 for Adience and 10 5 for DR. |