Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Translation Synchronization via Truncated Least Squares
Authors: Xiangru Huang, Zhenxiao Liang, Chandrajit Bajaj, Qixing Huang
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real datasets show that Tran Sync is superior to state-of-the-art convex formulations in terms of both efficiency and accuracy. |
| Researcher Affiliation | Academia | Xiangru Huang The University of Texas at Austin 2317 Speedway, Austin, 778712 EMAIL Zhenxiao Liang Tsinghua University Beijing, China, 100084 EMAIL Chandrajit Bajaj The University of Texas at Austin 2317 Speedway, Austin, 778712 EMAIL Qixing Huang The University of Texas at Austin 2317 Speedway, Austin, 778712 EMAIL |
| Pseudocode | Yes | Algorithm 1 Tran Sync(c, kmax) |
| Open Source Code | No | The paper does not provide any specific links or explicit statements about releasing source code for the described methodology. |
| Open Datasets | Yes | We utilize the Patriot Circle Lidar dataset1. 1http://masc.cs.gmu.edu/wiki/Map GMU |
| Dataset Splits | No | The paper describes the stopping condition for its iterative algorithm, but it does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies used in the experiments. |
| Experiment Setup | Yes | For this experiment, instead of using kmax as stopping condition as in Algorithm 1, we stop when we observe δk < δmin. Here δmin does not need to be close to σ. In fact, we choose δmin = 0.05, 0.1 for σ = 0.01, 0.04, respectively. |