DCLP: Neural Architecture Predictor with Curriculum Contrastive Learning
Authors: Shenghe Zheng, Hongzhi Wang, Tianyu Mu
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate that DCLP has high accuracy and efficiency compared with existing predictors, and shows promising potential to discover superior architectures in various search spaces when combined with search strategies. |
| Researcher Affiliation | Academia | Massive Data Computing Lab, Harbin Institute of Technology shenghez.zheng@gmail.com, {wangzh, mutianyu}@hit.edu.cn |
| Pseudocode | Yes | Algorithm 1: Training procedure of DCLP |
| Open Source Code | Yes | Our code is available at: https://github.com/Zhengsh123/DCLP. |
| Open Datasets | Yes | NAS-Bench-101. It is an OON set of 423k CNN (Ying et al. 2019). ... Test accuracy on CIFAR-10 is available within the search space. NAS-Bench-201. This is a NAS benchmark in OOE space (Dong and Yang 2020) with 15,625 structures, providing performance on CIFAR-10, CIFAR-100, and Image Net. DARTS Space. It is an OOE search space (Liu, Simonyan, and Yang 2019)... Language Model. ... DARTS RNN Space: ... PTB as the dataset for the language model task. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide details about validation dataset splits or how they are generated, such as percentages, sample counts, or specific methodology for reproduction. |
| Hardware Specification | Yes | We use a single RTX3090 as the platform. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as libraries or frameworks (e.g., PyTorch, TensorFlow, CUDA versions) used for the experiments. |
| Experiment Setup | No | We report our setups in Appendix D. |