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..
Scaling Spherical CNNs
Authors: Carlos Esteves, Jean-Jacques Slotine, Ameesh Makadia
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show our larger spherical CNNs reach state-of-the-art on several targets of the QM9 molecular benchmark, which was previously dominated by equivariant graph neural networks, and achieve competitive performance on multiple weather forecasting tasks. |
| Researcher Affiliation | Collaboration | 1Google Research, New York, NY, USA 2Nonlinear Systems Laboratoty, MIT, Cambridge, MA, USA. |
| Pseudocode | No | No pseudocode or algorithm blocks were explicitly labeled or presented in a structured format. |
| Open Source Code | Yes | Our code is available https://github.com/google-research/spherical-cnn. |
| Open Datasets | Yes | QM9 (Ramakrishnan et al., 2014), a current standard benchmark for this problem, contains 134K molecules... ERA5 reanalysis data (Hersbach et al., 2020) |
| Dataset Splits | Yes | There are two different splits used in the literature, the major difference being that Split 1 uses a training set of 110 000 elements while Split 2 uses 100 000. We train for 2000 epochs on 16 TPUv4 with batch size 16 |
| Hardware Specification | Yes | We train for 2000 epochs on 16 TPUv4 with batch size 16; training runs at around 37 steps/s. We evaluated our model for molecules (Section 5.1) on 8 V100 GPUs, with batch size of 1 per device, and it trains at 13.1 steps/s. |
| Software Dependencies | No | The paper mentions 'Tensor Flow (Abadi et al., 2016)' and 'JAX (Bradbury et al., 2018)' but does not specify their version numbers. |
| Experiment Setup | Yes | We train for 2000 epochs on 16 TPUv4 with batch size 16; training runs at around 37 steps/s. We use the Adam (Kingma & Ba, 2014) optimizer and a cosine decay on the learning rate with one epoch linear warmup in all experiments. |