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..
HexaConv
Authors: Emiel Hoogeboom, Jorn W.T. Peters, Taco S. Cohen, Max Welling
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on the CIFAR-10 benchmark and on the Aerial Image Dataset (AID) (Xia et al., 2017). The CIFAR-10 results are presented in Table 1, obtained by taking the average of 10 experiments with different random weight initializations. |
| Researcher Affiliation | Academia | Emiel Hoogeboom , Jorn W.T. Peters & Taco S. Cohen University of Amsterdam EMAIL Max Welling University of Amsterdam & CIFAR EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks are provided in the paper. |
| Open Source Code | Yes | Source code of G-Hexa Convs is available on Github: https://github.com/ehoogeboom/hexaconv. |
| Open Datasets | Yes | We evaluate our method on the CIFAR-10 benchmark and on the Aerial Image Dataset (AID) (Xia et al., 2017). |
| Dataset Splits | No | The paper mentions splitting data into '80% train/20% test sets' for AID but does not specify a validation split or its proportion for either dataset. |
| Hardware Specification | No | The paper does not specify any particular hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, TensorFlow 1.x). |
| Experiment Setup | No | The paper describes network architectures (e.g., '3 stages, with 4 blocks per stage', 'first convolution layer has stride two') but does not provide specific hyperparameter values like learning rate, batch size, or number of epochs for training. |