Estimating Riemannian Metric with Noise-Contaminated Intrinsic Distance
Authors: Jiaming Qiu, Xiongtao Dai
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We develop theoretical foundation for our method by deriving the rates of convergence for the asymptotic bias and variance of the estimated metric tensor. The proposed method is shown to be versatile in simulation studies and real data applications involving taxi trip time in New York City and MNIST digits. |
| Researcher Affiliation | Academia | Jiaming Qiu Fred Hutchinson Cancer Center jqiu3@fredhutch.org Xiongtao Dai Division of Biostatistics, School of Public Health University of California, Berkeley xdai@berkeley.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | New York City Taxi and Limousine Commission (TLC) Trip Record Data was accessed on May 1st, 20223 to obtained business day morning taxi trip records including GPS coordinates for pickup/dropoff locations as (Xu0, Xu1) and trip duration as Yu. We embed images in MNIST to a 2-dimensional space via t SNE [13]. |
| Dataset Splits | No | The paper describes using simulation and real-world datasets but does not explicitly provide details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper discusses the theoretical framework and types of models used, including the role of bandwidth 'h', but does not provide concrete hyperparameter values or specific system-level training configurations for its experiments. |