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.
Towards Croppable Implicit Neural Representations
Authors: Maor Ashkenazi, Eran Treister
NeurIPS 2024 | Venue PDF | LLM Run Details | Input Tokens: 28,211 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 7,549 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on various signal encoding tasks and compare it to alternatives. |
| Researcher Affiliation | Academia | Maor Ashkenazi Ben-Gurion University of the Negev EMAIL Eran Treister Ben-Gurion University of the Negev EMAIL |
| Pseudocode | Yes | Algorithm 1 Automatic Partitioning |
| Open Source Code | Yes | Code is available at https://github.com/maorash/Local-Global-INRs. |
| Open Datasets | Yes | Next, from the DIV2K dataset [2], we have randomly selected a subset of 25 images which were downsampled by a factor of four before training. |
| Dataset Splits | No | The paper discusses training and testing but does not explicitly describe the use of a validation set or its split ratios for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | We ran the experiments multiple times on a single Nvidia RTX3090. |
| Software Dependencies | No | The paper mentions using 'Adam W scheduler [26]' and 'Adam optimizer [22]', but does not specify version numbers for general software dependencies or libraries (e.g., PyTorch version, Python version). |
| Experiment Setup | Yes | The full configuration and networks size for all experiments is in Appendix B. |