GLAD: Learning Sparse Graph Recovery
Authors: Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, Le Song
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we show that the AM architecture provides very good inductive bias, allowing the model to learn very effective sparse graph recovery algorithm with a small amount of training data.In this section, we report several experiments to compare GLAD with traditional algorithms and other data-driven algorithms. |
| Researcher Affiliation | Collaboration | {1School of Computational Science & Engineering, 2School of Mathematics, 3School of Industrial and Systems Engineering }at Georgia Institute of Technology, 4Computer Science Department at Northwestern University, 5Ant Financial Services Group |
| Pseudocode | Yes | Algorithm 1: GLAD and Algorithm 2: ADMMu |
| Open Source Code | Yes | Python implementation (tested on P100 GPU) is available1. 1code: https://drive.google.com/open?id=16POE4TMp7UUie_Lc_Lq_Rz_STqzk_VHm2stl_M |
| Open Datasets | Yes | For sections 5.1 and 5.2, the synthetic data was generated based on the procedure described in Rolfs et al. (2012).The Syn TRe N (Van den Bulcke et al., 2006) is a synthetic gene expression data generator specifically designed for analyzing the structure learning algorithms.We use the real data from the DREAM 5 Network Inference challenge (Marbach et al., 2012). |
| Dataset Splits | Yes | For finetuning the traditional algorithms, a validation dataset of 10 graphs was used. For the GLAD algorithm, 10 training graphs were randomly chosen and the same validation set was used. |
| Hardware Specification | Yes | Python implementation (tested on P100 GPU) is available1.Convergence results and average runtime of different algorithms on Nvidia s P100 GPUs are shown in Figure 4 and Table 2 respectively. |
| Software Dependencies | No | The paper mentions 'Python implementation' and 'sklearn package Pedregosa et al. (2011)' but does not provide specific version numbers for Python, scikit-learn, or any other software libraries. |
| Experiment Setup | Yes | GLAD parameter settings: ρnn was a 4 layer neural network and Λnn was a 2 layer neural network. Both used 3 hidden units in each layer. The non-linearity used for hidden layers was tanh, while the final layer had sigmoid (σ) as the non-linearity for both, ρnn and Λnn (refer Figure 3). The learnable offset parameter of initial Θ0 was set to t = 1. It was unrolled for L = 30 iterations. The learning rates were chosen to be around [0.01, 0.1] and multi-step LR scheduler was used. The optimizer used was adam. |