Symbolic Learning to Optimize: Towards Interpretability and Scalability
Authors: Wenqing Zheng, Tianlong Chen, Ting-Kuei Hu, Zhangyang Wang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The evaluation results are reported in Fig.2 and Table 4. |
| Researcher Affiliation | Academia | 1The University of Texas at Austin, 2Texas A&M University |
| Pseudocode | Yes | Figure 7: The pseudo-code for the core mutation operation used in the SR algorithm. |
| Open Source Code | Yes | Codes are available at: https: //github.com/VITA-Group/Symbolic-Learning-To-Optimize. |
| Open Datasets | Yes | The L2O meta-training was performed on multiple randomly-initialized Le Nets on the MNIST dataset. ... Res Net-50, Res Net-152 and Mobile Net V2, on the Cifar-10 and Cifar-100 datasets. |
| Dataset Splits | No | No explicit percentages or sample counts for training, validation, and test splits of the datasets (MNIST, CIFAR-10, CIFAR-100) were found in the paper's main text. Although a 'validation set' is mentioned for symbolic distillation, the overall dataset splits for model training are not specified. |
| Hardware Specification | Yes | The execution times for SR and computing TPF/MC are measured on the 2.6 GHz Intel Core i7 CPU with 16 GB 2400 MHz DDR4 Memory, and other other computation times are measured on the Nvidia A6000 GPU. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, specific libraries or solvers with versions) were explicitly stated in the paper. |
| Experiment Setup | Yes | We used 128 batch size, and in both meta-fine-tune phase and final evaluation phase, we trained the CNN optimizees for 200 epochs. ... The number of iterations: ...300... The population number: ...200... Dataset size: ...5000 samples... For both optimizers, we used lr = 0.0005. For Adam W, we used β1 = 0.9, β2 = 0.999. |