Exploring the Complexity of Deep Neural Networks through Functional Equivalence
Authors: Guohao Shen
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Leveraging the equivalence property, we present a novel bound on the covering number for deep neural networks, which reveals that the complexity of neural networks can be reduced. Additionally, we demonstrate that functional equivalence benefits optimization, as overparameterized networks tend to be easier to train since increasing network width leads to a diminishing volume of the effective parameter space. These findings can offer valuable insights into the phenomenon of overparameterization and have implications for understanding generalization and optimization in deep learning. |
| Researcher Affiliation | Academia | 1Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China. Correspondence to: Guohao Shen <guohao.shen@polyu.edu.hk>. |
| Pseudocode | No | The paper focuses on theoretical analysis and mathematical proofs, and 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. It is a theoretical paper primarily focused on mathematical derivations and analysis. |
| Open Datasets | No | The paper is theoretical and does not involve empirical evaluation on datasets, so no dataset information or access is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical evaluation or dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details, hyperparameters, or training configurations. |