Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Norm-Based Generalisation Bounds for Deep Multi-Class Convolutional Neural Networks
Authors: Antoine Ledent, Waleed Mustafa, Yunwen Lei, Marius Kloft8279-8287
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 20,104 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,801 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We show generalisation error bounds for deep learning with two main improvements over the state of the art. (1) Our bounds have no explicit dependence on the number of classes except for logarithmic factors. (2) We adapt the classic Rademacher analysis of DNNs to incorporate weight sharing a task of fundamental theoretical importance which was previously attempted only under very restrictive assumptions. |
| Researcher Affiliation | Academia | Antoine Ledent 1, Waleed Mustafa 1, Yunwen Lei 1,2 and Marius Kloft 1 1Department of Computer Science, TU Kaiserslautern, 67653 Kaiserslautern, Germany. 2School of Computer Science, University of Birmingham, Birmingham B15 2TT, United Kingdom |
| Pseudocode | No | The paper presents mathematical theorems and propositions but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention making any source code available for the described methodology. |
| Open Datasets | No | The paper is purely theoretical and does not describe or use any specific datasets for training or evaluation. It refers to "training examples" in a general theoretical context, but not a concrete dataset. |
| Dataset Splits | No | The paper is purely theoretical and does not describe any experimental setup or dataset splits (training, validation, test) for reproducibility. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is purely theoretical and does not describe any experimental setup, thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is purely theoretical and does not describe any experimental setup, hyperparameters, or training settings. |