Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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 and potential bias; scores should be interpreted as estimates.
Message Passing Least Squares Framework and its Application to Rotation Synchronization
Authors: Yunpeng Shi, Gilad Lerman
ICML 2020 | Venue PDF | LLM Run Details | Input Tokens: 22,573 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,989 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the superior performance of our algorithm over state-of-the-art methods for rotation synchronization using both synthetic and real data. |
| Researcher Affiliation | Academia | 1School of Mathematics, University of Minnesota, Minneapolis, MN, USA. Correspondence to: Yunpeng Shi<EMAIL>, Gilad Lerman<EMAIL>. |
| Pseudocode | Yes | Algorithm 1 CEMP (Lerman & Shi, 2019) ; Algorithm 2 Message Passing Least Squares (MPLS) ; Algorithm 3 MPLS-SO(3) |
| Open Source Code | No | The paper mentions using implementations by other researchers ('We use their implementation by Chatterjee & Govindu (2018)') but does not provide access to the authors' own source code for the described methodology. |
| Open Datasets | Yes | We compare the performance of the different algorithms on the Photo Tourism datasets (Wilson & Snavely, 2014). |
| Dataset Splits | No | The paper does not explicitly provide specific training/test/validation dataset splits (e.g., percentages, sample counts, or references to predefined splits) needed for reproduction. |
| Hardware Specification | Yes | All computational tasks were implemented on a machine with 2.5GHz Intel i5 quad core processors and 8GB memory. |
| Software Dependencies | No | The paper mentions using existing implementations by other authors (e.g., Chatterjee & Govindu), but it does not specify software dependencies with version numbers for its own implementation or experiments. |
| Experiment Setup | Yes | We use the following default parameters for Algorithm 1: |Cij|= 50 for ij E; T =5; βt=2t and t=0,...,5. If an edge is not contained in any 3-cycle, we set its corruption level as 1. For MPLSSO(3), which we refer to in this section as MPLS, we use the above parameters of Algorithm 1 and the following ones for t 1: αt=1/(t+1) and τt=inf x n ˆPt(x)>max{1 0.05t,0.8} o . ... F(x) for MPLS is chosen as x 3/2 and it corresponds to ρ(x)= x. ... We use the convergence criterion P i [n] Ωi,t F/( 2n)<0.001 of Chatterjee & Govindu (2018) for all the above algorithms. |