Learning to Optimize in Swarms
Authors: Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results over non-convex test functions and the protein-docking application demonstrate that this new meta-optimizer outperforms existing competitors. |
| Researcher Affiliation | Academia | Yue Cao, Tianlong Chen, Zhangyang Wang, Yang Shen Departments of Electrical and Computer Engineering & Computer Science and Engineering Texas A&M University, College Station, TX 77840 {cyppsp,wiwjp619,atlaswang,yshen}@tamu.edu |
| Pseudocode | No | The paper describes the algorithms and models using text and mathematical equations but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The codes are publicly available at: https://github.com/Shen-Lab/LOIS. |
| Open Datasets | Yes | We choose a training set of 25 protein-protein complexes from the protein docking benchmark set 4.0 [29] (see Supp. Table S1 for the list), each of which has 5 starting points (top-5 models from ZDOCK [30]). |
| Dataset Splits | No | The paper does not explicitly mention a validation set or describe a specific data split for validation. |
| Hardware Specification | No | Part of the computing time is provided by the Texas A&M High Performance Research. No specific hardware details (e.g., CPU/GPU models, memory) are mentioned. |
| Software Dependencies | No | To train our model we use the optimizer Adam which requires gradients. The first-order gradients are calculated numerically through Tensor Flow following [17]. The paper mentions TensorFlow and CHARMM but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | In our implementation the length of LSTM is set to be 20. For all experiments, the optimizer is trained for 10,000 epochs with 100 iterations in each epoch. The population size k of our meta-optimizer and PSO is set to be 4, 10 and 10 in the 2D, 10D and 20D cases, respectively. |