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..
Learning Polynomial Problems with $SL(2, \mathbb{R})$-Equivariance
Authors: Hannah Lawrence, Mitchell Tong Harris
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we demonstrate for the first time that neural networks can effectively solve such problems in a data-driven fashion, achieving tenfold speedups while retaining high accuracy. In our experiments, we compare several instantiations of equivariant learning. Timing Comparison: Trained Network vs Solver |
| Researcher Affiliation | Academia | Hannah Lawrence & Mitchell Tong Harris Massachusetts Institute of Technology |
| Pseudocode | Yes | Algorithm 1 SL(2, R)-equivariant architecture |
| Open Source Code | Yes | We have released all data generation (as well as training) code, so that future research may build on these preliminary benchmarks. can be found in the code at github.com/harris-mit/poly SL2equiv. |
| Open Datasets | No | The paper generates its own synthetic datasets based on described distributions and mathematical constructs (e.g., 'Random, rotationally symmetric' and 'Delsarte spherical code bounds') rather than utilizing an existing, pre-published public dataset. While the data generation code is provided, the datasets themselves are not described as pre-existing public resources with direct access information (e.g., a specific download link for the generated data). |
| Dataset Splits | Yes | We used 5, 000 training examples, 500 validation examples, and 500 test examples. |
| Hardware Specification | Yes | All experiments were run on Nvidia Volta V100 GPUs |
| Software Dependencies | No | The paper mentions software like the Ada M optimizer, Mosek, and SCS, but does not provide specific version numbers for these or other key software dependencies (e.g., Python, PyTorch, CUDA versions) necessary for replication. |
| Experiment Setup | Yes | All experiments were run on Nvidia Volta V100 GPUs, using the Ada M optimizer with learning rate 3 10 4. Experiments were trained for 700 epochs across 4 random seeds. We used the hyperparameters shown in Tables 3 and 4... |