Switching Autoregressive Low-rank Tensor Models
Authors: Hyun Dong Lee, Andrew Warrington, Joshua Glaser, Scott Linderman
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate quantitative advantages of SALT models on a range of simulated and real prediction tasks, including behavioral and neural datasets. |
| Researcher Affiliation | Academia | Hyun Dong Lee Computer Science Department Stanford University hdlee@stanford.edu Andrew Warrington Department of Statistics Stanford University awarring@stanford.edu Joshua I. Glaser Department of Neurology Northwestern University j-glaser@northwestern.edu Scott W. Linderman Department of Statistics Stanford University scott.linderman@stanford.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Source code is available at https://github.com/lindermanlab/salt. |
| Open Datasets | Yes | Wiltschko et al. [2015] collected videos of mice freely behaving in a circular open field. and We analyzed neural recordings of an immobilized C. elegans worm from Kato et al. [2015]. |
| Dataset Splits | Yes | The likelihood on a held-out validation set shows that the ARHMM overfitted quickly as the number of lags increased, while CP-SALT was more robust to overfitting (Figure 4B). We compared loglikelihoods of the best model (evaluated on the validation set) on a separate held-out test set and found that CP-SALT consistently outperformed ARHMM across mice (Figure 4C). |
| Hardware Specification | Yes | using 100 iterations of EM on a single NVIDIA Tesla P100 GPU. and using 100 iterations of EM on a single NVIDIA Tesla V100 GPU. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We used H = 50 discrete states and fitted ARHMMs and CP-SALT models with varying lags and ranks. We imposed stickiness on the discrete state transition matrix via a Dirichlet prior with concentration of 1.1 on non-diagonals and 6 104 on the diagonals. We trained each model 5 times with random initialization for each hyperparameter, using 100 iterations of EM on a single NVIDIA Tesla P100 GPU. and We fitted both single and multi-subspace CP-SALT models with ranks D {8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}. Similarly, SLDSs were fitted with the same range of latent dimension size. For both CP-SALT models and ARHMMs, we used L {1, 3, 6, 9} and the number of discrete states was set to H = 7 |