Gaussian Process Random Fields
Authors: David Moore, Stuart J. Russell
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then evaluate it on synthetic data as well as an application to seismic event location. |
| Researcher Affiliation | Academia | David A. Moore and Stuart J. Russell Computer Science Division University of California, Berkeley Berkeley, CA 94709 {dmoore, russell}@cs.berkeley.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It mentions using 'GPy [23]' which is a third-party framework, but does not release its own implementation. |
| Open Datasets | No | The paper describes synthetic data generation ("We sample n points uniformly from the square...") and refers to a specific real-world seismic dataset ("Our dataset consists of 107556 events detected at the Mankachi array station in Kazakstan between 2004 and 2012.") but does not provide concrete access information (link, DOI, formal citation for public availability) for either. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed splitting methodology) for training, validation, and testing. |
| Hardware Specification | No | The paper mentions memory requirements (e.g., 'exceeded 32GB of available memory', 'required approximately 30GB of RAM') but does not specify exact GPU/CPU models, processor types, or detailed computer specifications used for running experiments. |
| Software Dependencies | No | The paper mentions using 'GPy [23]' for the Sparse GP-LVM implementation, but does not provide specific version numbers for GPy or any other ancillary software components. |
| Experiment Setup | Yes | For local GPs and GPRFs, we take the spatial partition to be a grid with n/m cells, where m is the desired number of points per cell. The GPRF edge set E connects each cell to its eight neighbors (Figure 2c), yielding linear time complexity O(nm2). During optimization, a practical choice is necessary: do we use a fixed partition of the points, or re-assign points to cells as they cross spatial boundaries? |