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..
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. |