Improving Multi-Step Prediction of Learned Time Series Models
Authors: Arun Venkatraman, Martial Hebert, J.. Bagnell
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results of our method, DAD, and show significant improvement over the traditional approach in two notably different domains, dynamic system modeling and video texture prediction. |
| Researcher Affiliation | Academia | Arun Venkatraman, Martial Hebert, and J. Andrew Bagnell Robotics Institute, Carnegie Mellon University, Pittsburgh, PA 15213 arunvenk@cs.cmu.edu, {hebert, dbagnell}@ri.cmu.edu |
| Pseudocode | Yes | Algorithm 1 DATA AS DEMONSTRATOR (DAD) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | The final dynamical system we test on is the simulated helicopter from (Abbeel and Ng 2005a). |
| Dataset Splits | No | Algorithm 1 mentions 'return Mn with lowest error on validation trajectories' but the experimental setup does not specify the size or split method for validation data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Random Fourier Feature Regression' and 'Gaussian kernel' but does not provide specific version numbers for any software components. |
| Experiment Setup | Yes | In the experiments described below, we choose m = 500 and use the Gaussian kernel. The hyperparameters λ and kernel bandwidth σ were chosen using cross-validated grid search. |