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].
Exponential Separations in Symmetric Neural Networks
Authors: Aaron Zweig, Joan Bruna
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we demonstrate a novel separation between symmetric neural network architectures. Specifically, we consider the Relational Network [21] architecture as a natural generalization of the Deep Sets [32] architecture, and study their representational gap. Under the restriction to analytic activation functions, we construct a symmetric function acting on sets of size N with elements in dimension D, which can be efficiently approximated by the former architecture, but provably requires width exponential in N and D for the latter. |
| Researcher Affiliation | Academia | Aaron Zweig Courant Institute of Mathematical Sciences New York University EMAIL Joan Bruna Center for Data Science New York University EMAIL |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as |
| Open Source Code | No | The checklist at the end of the paper states 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]'. |
| Open Datasets | No | The paper is theoretical and does not mention any datasets used for training. The author checklist explicitly marks 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]'. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits (train, validation, test). The author checklist explicitly marks 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]'. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for experiments. The author checklist explicitly marks 'Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]'. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers for experimental reproducibility. The author checklist explicitly marks 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A]'. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training settings. The author checklist explicitly marks 'Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A]'. |