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..
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Authors: Maurice Weiler, Mario Geiger, Max Welling, Wouter Boomsma, Taco S. Cohen
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results confirm the effectiveness of 3D Steerable CNNs for the problem of amino acid propensity prediction and protein structure classification, both of which have inherent SE(3) symmetry. |
| Researcher Affiliation | Collaboration | Maurice Weiler* University of Amsterdam EMAIL Mario Geiger* EPFL EMAIL Max Welling University of Amsterdam, CIFAR, Qualcomm AI Research EMAIL Wouter Boomsma University of Copenhagen EMAIL Taco Cohen Qualcomm AI Research EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is available at https://github.com/mariogeiger/se3cnn. |
| Open Datasets | Yes | We constructed a new data set, based on the CATH protein structure classification database [11], version 4.2 (see http://cathdb.info/browse/tree). ... The new dataset is available at https://github.com/wouterboomsma/cath_datasets. |
| Dataset Splits | Yes | We used the first seven of the ten splits for training, the eighth for validation and the last two for testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. It mentions 'current hardware' generally. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer [25]' but does not provide specific version numbers for any software components, programming languages, or libraries used for implementation. |
| Experiment Setup | Yes | We train the models for 100 epochs using the Adam optimizer [25], with an exponential learning rate decay of 0.94 per epoch starting after an initial burn-in phase of 40 epochs. |