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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Band-Limited Gaussian Processes: The Sinc Kernel
Authors: Felipe Tobar
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The developed theory is complemented with illustrative graphic examples and validated experimentally using real-world data. and Section 6 validates the proposed kernel through numerical experiments with real-world signals. |
| Researcher Affiliation | Academia | Felipe Tobar Center for Mathematical Modeling Universidad de Chile EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'the sinc function implemented in Python' but does not provide concrete access to source code for the methodology described in the paper. |
| Open Datasets | Yes | MIT-BIH database [7], TIMIT repository [6], Mauna Loa monthly CO2 concentration series |
| Dataset Splits | Yes | Using one third of the data, training the GP-sinc (plus noise variance) was achieved by maximum likelihood... and We focused on the reconstruction setting using only 200 (out of 1000) observations with added Gaussian noise... and used a subset of 1200 (out of 9000) observations samples... |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'the sinc function implemented in Python' but does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | Using one third of the data, training the GP-sinc (plus noise variance) was achieved by maximum likelihood, were both the BFGS [33] and Powell [20] optimisers yielded similar results. and with added Gaussian noise of standard deviation equal to a 10% of that of the audio signal. and using carrier of frequency 2Hz (most of the power of the heart-rate signals is contained below 1Hz), and used a subset of 1200 (out of 9000) observations samples with added noise of standard deviation equal to a 20% of that of the modulated signal. |