XOR-CD: Linearly Convergent Constrained Structure Generation
Authors: Fan Ding, Jianzhu Ma, Jinbo Xu, Yexiang Xue
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our real-world experiments on datadriven experimental design, dispatching route generation, and sequence-based protein homology detection demonstrate the superior performance of XOR-CD compared to baseline approaches in generating valid structures as well as capturing the inductive bias in the training set. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Purdue University, West Lafayette, USA 2Institute for Artificial Intelligence, Peking University, Beijing, China 3Toyota Technological Institute at Chicago, Illinois, USA. |
| Pseudocode | Yes | The detailed procedure of XOR-CD is shown in Algorithm 1. ... Algorithm 1 XOR-CD |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the described methodology. |
| Open Datasets | Yes | We constructed the training set from the PDB40 dataset (Wu & Xu, 2020). |
| Dataset Splits | Yes | The test set is made up with 50 randomly sampled sequences from the PDB40 dataset separated from the training set. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud instance types used for the experiments. |
| Software Dependencies | No | The paper mentions general software like Python but does not provide specific version numbers for libraries or tools used in their experimental setup. |
| Experiment Setup | Yes | Learning rate is fixed as 0.1 and total number of epochs T is 500. There is also a timeout of 10 hours for all algorithms. We also set both M and K to be 100, and parameters for XOR-Sampling were kept the same as in (Ermon et al., 2013b). |