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].
On the Universality of Invariant Networks
Authors: Haggai Maron, Ethan Fetaya, Nimrod Segol, Yaron Lipman
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We present two main results: First, G-invariant networks are universal if high-order tensors are allowed. Second, there are groups G for which higher-order tensors are unavoidable for obtaining universality. G-invariant networks consisting of only ๏ฌrst-order tensors are of special interest due to their practical value. We conclude the paper by proving a necessary condition for the universality of G-invariant networks that incorporate only ๏ฌrst-order tensors. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel 2Department of Computer Science, University of Toronto, Toronto, Canada 3Vector Institute. Correspondence to: Haggai Maron <EMAIL>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve training models on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |