Supervised Learning for Dynamical System Learning
Authors: Ahmed Hefny, Carlton Downey, Geoffrey J. Gordon
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated the above models using 1000 random splits of the 325 sequences into 200 training and 125 testing. For each testing observation ot we compute the absolute error between actual and expected value (i.e. |δot=1 P(ot = 1 | o1:t 1)|). We report the mean absolute error for each split. The results are displayed in Figure 4. |
| Researcher Affiliation | Academia | Ahmed Hefny Carnegie Mellon University Pittsburgh, PA 15213 ahefny@cs.cmu.edu Carlton Downey Carnegie Mellon University Pittsburgh, PA 15213 cmdowney@cs.cmu.edu Geoffrey J. Gordon Carnegie Mellon University Pittsburgh, PA 15213 ggordon@cs.cmu.edu |
| Pseudocode | No | The paper describes steps in a detailed, bulleted list under 'Model Specification', 'S1A Regression', 'S1B Regression', 'S2 Regression', 'Initial State Estimation', and 'Inference' but does not present them as structured pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | Yes | We evaluate the model using the Geometry Area (1996-97) data available from Data Shop [20]. [20] Kenneth R. Koedinger, R. S. J. Baker, K. Cunningham, A. Skogsholm, B. Leber, and John Stamper. A data repository for the EDM community: The PSLC Data Shop. Handbook of Educational Data Mining, pages 43 55, 2010. |
| Dataset Splits | Yes | We evaluated the above models using 1000 random splits of the 325 sequences into 200 training and 125 testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'MATLAB’s built-in logistic regression and EM functions' but does not specify version numbers for MATLAB or any other software components. |
| Experiment Setup | Yes | We compare three models that differ by history features and S1 regression method: Spec-HMM: This baseline uses ht = ot 1 and linear S1 regression... Feat-HMM: This baseline represents ht by an indicator vector of the joint assignment of the previous b observations (we set b to 4) and uses linear S1 regression... LR-HMM: This model represents ht by a binary vector of length b encoding the previous b observations and uses logistic regression as the S1 model. |