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 [1].
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. |