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 Meta-Pruning via Optimal Transport
Authors: Alexander Theus, Olin Geimer, Friedrich Wicke, Thomas Hofmann, Sotiris Anagnostidis, Sidak Pal Singh
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 47,336 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,448 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 benchmark our results for various networks on commonly used datasets such as CIFAR-10, CIFAR-100, and Image Net.Here, we seek to illustrate the accuracy gains Intra-Fusion can achieve. ... we compare the test accuracy of a VGG11BN, Res Net18, Res Net50, on CIFAR-10, CIFAR-100, and Image Net. |
| Researcher Affiliation | Academia | Department of Computer Science ETH Zurich, Switzerland |
| Pseudocode | Yes | we show both meta-pruning approaches side by side (see Algorithm 1 and 2) to highlight the differences between the two. |
| Open Source Code | Yes | Our code is available here1. 1Github repository: https://github.com/alexandertheus/Intra-Fusion. |
| Open Datasets | Yes | We benchmark our results for various networks on commonly used datasets such as CIFAR-10, CIFAR-100, and Image Net. |
| Dataset Splits | Yes | In the approach we want to propose, we split the data set into two subsets a and b on which we then train two individual models modela, modelb in parallel.Specifically, see Performance Comparison: After Convergence (Appendix D.2) and Performance Comparison: Varying Fine-Tuning (Appendix D.3). An extension to combining more than the presented two datasets can be found in k-Fold Split-Data (Appendix D.4). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as exact GPU/CPU models or processor types. Figure 22 only vaguely mentions "Nvidia Gpu" without further specification. |
| Software Dependencies | No | The paper does not list specific version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | During the training of the used VGG11-BN and Resnet18 networks, we deploy the training hyperparameters in Table 1. For the fine-tuning of models after pruning we use the hyperparameters in Table 2. |