Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Structured Inference Networks for Nonlinear State Space Models
Authors: Rahul Krishnan, Uri Shalit, David Sontag
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 18,391 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,752 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the learning algorithm to both synthetic and real-world datasets, demonstrating its scalability and versatility. We find that using the structured approximation to the posterior results in models with significantly higher held-out likelihood. |
| Researcher Affiliation | Academia | Rahul G. Krishnan, Uri Shalit, David Sontag Courant Institute of Mathematical Sciences, New York University EMAIL |
| Pseudocode | Yes | Algorithm 1 depicts an overview of the learning algorithm. |
| Open Source Code | Yes | Code for learning DMMs and reproducing our results may be found at: github.com/clinicalml/structuredinference |
| Open Datasets | Yes | Polyphonic Music: We train DMMs on polyphonic music data (Boulanger-lewandowski, Bengio, and Vincent 2012). |
| Dataset Splits | Yes | We learn for 2000 epochs and report results based on early stopping using the validation set. |
| Hardware Specification | Yes | The Tesla K40s used for this research were donated by the NVIDIA Corporation. |
| Software Dependencies | Yes | Our models and learning algorithm are implemented in Theano (Theano Development Team 2016). We use Adam (Kingma and Ba 2015) with a learning rate of 0.0008 to train the DMM. |
| Experiment Setup | Yes | We use Adam (Kingma and Ba 2015) with a learning rate of 0.0008 to train the DMM. |