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..
A Functional Dynamic Boltzmann Machine
Authors: Hiroshi Kajino
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We design numerical experiments to empirically confirm the effectiveness of our solutions. The experimental results demonstrate consistent error reductions as compared to baseline methods, from which we conclude the effectiveness of F-Dy BM for functional time series prediction. The effectiveness of F-Dy BM is empirically demonstrated using five real spatiotemporal data sets. |
| Researcher Affiliation | Industry | Hiroshi Kajino IBM Research Tokyo EMAIL |
| Pseudocode | Yes | Algorithm 1 An online learning algorithm for F-Dy BM. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | NOAA Global Surface Temperature V4.01 We use a temperature data set provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA. It contains the global temperature anomalies from January 1880 to present, resulting in the length 1638.3 Retrieved from http://www.esrl.noaa.gov/psd/ data/gridded/data.noaaglobaltemp.html on Aug. 23, 2016. Air Data is maintained by the United States Environmental Protection Agency, and we retrieved it from Air Quality System Data Mart (http://aqsdr1.epa.gov/aqsweb/aqstmp/ airdata/download_files.html) on Dec. 28, 2016. |
| Dataset Splits | Yes | To do so, we first divide the time series into training, validation, and test sets in chronological order with ratio 3 : 3 : 4. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | For all the models, we used the RBF kernel K(x, x ; γ) = exp( γ x x 2), and prepared the instances by all the combinations of the following hyperparameters: N = 25, d = 3, η[0] {2 n}23 n=19, σ2 {2 n}2 n=0, γ {2n}5 n=0 for all the models and L = 3, λ1 = 0.1, λ2 = 0.5, λ3 = 0.9 for G-Dy BM and F-Dy BM. All the models are trained by using SGD, and the learning rate is controlled by rmsprop [Tieleman and Hinton, 2012]. |