LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning
Authors: Chonghao Sima, Yexiang Xue
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we demonstrate that LSH-SMILE simulates physics systems at comparable quality with exact approaches, but with way less time and space complexity. |
| Researcher Affiliation | Academia | Chonghao Sima Department of Computer Science Purdue University West Lafayette, IN, USA, 47907 simac@purdue.edu Yexiang Xue Department of Computer Science Purdue University West Lafayette, IN, USA, 47907 yexiang@purdue.edu |
| Pseudocode | Yes | The pseudo code of LSH-SMILE for forward simulation is in Figure 4. |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] they will be provided in the supplementary material |
| Open Datasets | No | The dataset is synthetic and contains 1700 frames of grain growth ground truth simulation results. ... We use our own synthetic data |
| Dataset Splits | No | The paper mentions training epochs but does not explicitly detail train/validation/test dataset splits within the main text. The checklist states 'they will be provided in the supplementary material' but this is not in the main paper content. |
| Hardware Specification | No | The paper provides running times and memory usage in Table 1, but does not specify the exact hardware components (e.g., CPU, GPU models, or memory capacity) used for the experiments in the main text. |
| Software Dependencies | No | The paper mentions using the 'Torch framework' but does not specify its version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | We set dt to 0.05 in this simulation. The image size is 128 by 128. For LSH parameter, r and r0 are set to be 0.01, K is 3 and L is 10. ... We set dt to 0.1 in this simulation. The image size is 128 by 128. For LSH parameter, r is set to be 0.0001, K is 3 and L is 10. ... LSH-SMILE uses stochastic gradient descent, while the baseline use the Adam optimizer in our experiment. |