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].
Implicit Convolutional Kernels for Steerable CNNs
Authors: Maksim Zhdanov, Nico Hoffmann, Gabriele Cesa
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove the effectiveness of our method on multiple tasks, including N-body simulations, point cloud classification and molecular property prediction. In this section, we implement Steerable CNNs with implicit kernels and apply them to various tasks. |
| Researcher Affiliation | Collaboration | Maksim Zhdanov AMLab, University of Amsterdam EMAIL Nico Hoffmann Helmholtz-Zentrum Dresden-Rossendorf Gabriele Cesa Qualcomm AI Research AMLab, University of Amsterdam |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data to reproduce all experiments are available on Git Hub. |
| Open Datasets | Yes | Dataset. We conduct experiments using the N-body system [23]... Dataset. The Model Net-40 [56] dataset... Dataset. The QM9 dataset [55] is a public dataset... |
| Dataset Splits | Yes | From the remaining objects, we take 80% for training and 20% for validation. |
| Hardware Specification | No | All the experiments were performed using the Hemera compute cluster of Helmholtz-Zentrum Dresden-Rossendorf and the Iv I cluster of the University of Amsterdam. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | For each task, we tune hyperparameters of all models on validation data: the number of layers, the number of channels in each layer, the depth and the width of implicit kernels, and the number of training epochs. |