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.
Tropical Geometry of Deep Neural Networks
Authors: Liwen Zhang, Gregory Naitzat, Lek-Heng Lim
ICML 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,164 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,696 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 | The goal of our work is to establish connections between neural network and tropical geometry in the hope that they will shed light on the workings of deep neural networks. All proofs are deferred to Section D of the supplement. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Chicago, Chicago, IL 2Department of Statistics, University of Chicago, Chicago, IL 3Computational and Applied Mathematics Initiative, University of Chicago, Chicago, IL. |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found. |
| Open Source Code | No | No statement regarding the release of source code or a link to a code repository was found. |
| Open Datasets | No | This paper is theoretical and does not involve dataset evaluation or training, so no information about publicly available datasets or access details is provided. |
| Dataset Splits | No | This paper is theoretical and does not involve experimental validation, so no information about dataset splits for training, validation, or testing is provided. |
| Hardware Specification | No | This paper is theoretical and does not conduct experiments requiring specific hardware, so no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not describe computational experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not involve empirical experiments, thus no details on experimental setup, hyperparameters, or training configurations are provided. |