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.
Parabolic Approximation Line Search for DNNs
Authors: Maximus Mutschler, Andreas Zell
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,950 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,733 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 performed a comprehensive evaluation to analyze the performance of PAL on a variety of deep learning optimization tasks. Therefore, we tested PAL on commonly used architectures on CIFAR-10 [31], CIFAR-100 [31] and Image Net [13]. ... All in all, we trained over 4500 networks with Tensorflow 1.15 [1] on Nvidia Geforce GTX 1080 TI graphic cards. |
| Researcher Affiliation | Academia | Maximus Mutschler and Andreas Zell University of Tübingen Sand 1, D-72076 Tübingen, Germany EMAIL |
| Pseudocode | Yes | Algorithm 1 The basic version of our proposed line search algorithm. See Section 4 for details. ... The full version of PAL including all additions is given in Appendix B Algorithm 2. |
| Open Source Code | No | The paper mentions using "TensorFlow 1.15" but does not state that the authors' own implementation code for PAL is open-source or provide a link to it. |
| Open Datasets | Yes | We focus on CIFAR-10, as it is extensively analyzed in optimization research for deep learning. However, on random samples of MNIST, CIFAR-100 and Image Net we observed the same results. ... We performed a comprehensive evaluation to analyze the performance of PAL on a variety of deep learning optimization tasks. Therefore, we tested PAL on commonly used architectures on CIFAR-10 [31], CIFAR-100 [31] and Image Net [13]. |
| Dataset Splits | No | The paper mentions using validation accuracy but does not specify the explicit training, validation, and test dataset splits (e.g., percentages or exact counts) needed for reproduction. |
| Hardware Specification | Yes | All in all, we trained over 4500 networks with Tensorflow 1.15 [1] on Nvidia Geforce GTX 1080 TI graphic cards. |
| Software Dependencies | Yes | All in all, we trained over 4500 networks with Tensorflow 1.15 [1]... |
| Experiment Setup | Yes | A detailed explanation of the experiments including hyperparameters and data augmentations used are given in Appendix D.8. |