Structured Inference Networks for Nonlinear State Space Models
Authors: Rahul Krishnan, Uri Shalit, David Sontag
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {rahul, shalit, dsontag}@cs.nyu.edu |
| 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. |