Parabolic Approximation Line Search for DNNs
Authors: Maximus Mutschler, Andreas Zell
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {maximus.mutschler, andreas.zell}@uni-tuebingen.de |
| 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. |