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..
Sparse Gaussian Conditional Random Fields on Top of Recurrent Neural Networks
Authors: Xishun Wang, Minjie Zhang, Fenghui Ren
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Co R is evaluated by both synthetic data and real-world data, and it shows a significant improvement in performance over state-of-the-art methods. |
| Researcher Affiliation | Academia | Xishun Wang, Minjie Zhang, Fenghui Ren School of Computing and Information Technology, University of Wollongong, 2522, NSW, Australia. EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1 Alternative training of Co R |
| Open Source Code | No | The paper mentions using Theano and Lasagne for implementation, and provides a link to the NPower Forecasting Challenge data and results, but does not explicitly state that the source code for their proposed Co R model is publicly available or provide a link to it. |
| Open Datasets | Yes | We apply Co R to an electricity demand prediction problem, which is a competition called NPower Forecasting Challenge 2016. This competition adopted a rolling forecasting mode to simulate the real-world scenario. [...] 1https://www.npowerjobs.com/graduates/forecastingchallenge. Data are publicly available. |
| Dataset Splits | No | In each evaluation, random 80% samples are used for training, while the rest are for testing. |
| Hardware Specification | Yes | Evaluations are conducted on a server with 8 CPUs and 64 GB Memory. |
| Software Dependencies | No | In implementations, we use Theano (Theano Development Team 2016) and Lasagne (Dieleman et al. 2015) for deep neural network. |
| Experiment Setup | Yes | The synthetic data are generated as follows. The dimension of feature D is fixed as 10, while the time step T can be varied in {10, 20, 40}, and number of samples N can be varied in {1000, 2000, 4000, 8000}. |