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..
Expressive Sign Equivariant Networks for Spectral Geometric Learning
Authors: Derek Lim, Joshua Robinson, Stefanie Jegelka, Haggai Maron
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our theoretical results, we conduct various numerical experiments on synthetic datasets. Experiments in link prediction, n-body problems, and node clustering in graphs support our theory and demonstrate the utility of sign equivariant models. |
| Researcher Affiliation | Collaboration | Derek Lim MIT CSAIL EMAIL Joshua Robinson Stanford University Stefanie Jegelka TU Munich, MIT CSAIL Haggai Maron Technion, NVIDIA |
| Pseudocode | No | The paper describes methods in prose and mathematical formulations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | Our codes for our models and experiments will be open-sourced and permissively licensed. |
| Open Datasets | Yes | We test models on the CLUSTER dataset [Dwivedi et al., 2022a] for semi-supervised node clustering (viewed as node classification) in synthetic graphs. ... We follow the experimental setting and build on the code of Puny et al. [2022] (no license as far as we can tell) for the n-body learning task. The code for generating the data stems from Kipf et al. [2018] (MIT License) and Fuchs et al. [2020] (MIT License). |
| Dataset Splits | Yes | The train/validation/test split is 80%/10%/10%, and is chosen uniformly at random. |
| Hardware Specification | Yes | Each experiment was run on a single NVIDIA V100 GPU with 32GB memory. |
| Software Dependencies | No | The paper mentions software like Network X, Adam optimizer, and Graph GPS framework, but does not provide specific version numbers for these software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | Yes | We train each method for 100 epochs with an Adam optimizer [Kingma and Ba, 2015] at a learning rate of .01. |