Discriminative State Space Models
Authors: Vitaly Kuznetsov, Mehryar Mohri
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude with some promising preliminary experimental results in Appendix D. |
| Researcher Affiliation | Collaboration | Vitaly Kuznetsov Google Research New York, NY 10011, USA vitaly@cims.nyu.edu Mehryar Mohri Courant Institute and Google Research New York, NY 10011, USA mohri@cims.nyu.edu |
| Pseudocode | Yes | Figure 1: Pseudocode of the BOOSTSM algorithm. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions "observed realization (X1, Y1), . . . , (XT , YT ) of some stochastic process" and "preliminary experimental results in Appendix D" but does not provide concrete access information (link, citation, repository) for a publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) needed to reproduce the data partitioning for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions "preliminary experimental results in Appendix D" but does not provide specific experimental setup details, such as concrete hyperparameter values, training configurations, or system-level settings, in the main text. |