Predictive State Recurrent Neural Networks
Authors: Carlton Downey, Ahmed Hefny, Byron Boots, Geoffrey J. Gordon, Boyue Li
NeurIPS 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply PSRNNs to 4 datasets, and show that we outperform several popular alternative approaches to modeling dynamical systems in all cases. |
| Researcher Affiliation | Academia | Carlton Downey Carnegie Mellon University Pittsburgh, PA 15213 EMAIL Ahmed Hefny Carnegie Mellon University Pittsburgh, PA, 15213 EMAIL Boyue Li Carnegie Mellon University Pittsburgh, PA, 15213 EMAIL Byron Boots Georgia Tech Atlanta, GA, 30332 EMAIL Geoff Gordon Carnegie Mellon University Pittsburgh, PA, 15213 EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions 'e.g., a Py Torch implementation of this architecture for text prediction can be found at https://github.com/pytorch/examples/tree/master/word_language_model.', but this refers to a general PyTorch example, not the authors' specific open-source code for their PSRNN methodology. |
| Open Datasets | Yes | Penn Tree Bank (PTB) This is a standard benchmark in the NLP community [36]. Handwriting This is a digit database available on the UCI repository [37, 38] created using a pressure sensitive tablet and a cordless stylus. Swimmer We consider the 3-link simulated swimmer robot from the open-source package Open AI gym.3 |
| Dataset Splits | No | The paper specifies 'train/test split' for all datasets (e.g., 'Penn Tree Bank (PTB) ... train/test split of 120780/124774 characters.') but does not explicitly mention a validation split or its size. |
| Hardware Specification | No | The paper mentions 'Due to hardware limitations' but does not provide specific details about the hardware used (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper mentions 'Py Torch or Tensor Flow' as neural network libraries but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In two-stage regression we use a ridge parameter of 10( 2)n where n is the number of training examples... We use a horizon of 1 in the PTB experiments, and a horizon of 10 in all continuous experiments. We use 2000 RFFs from a Gaussian kernel... We use 20 hidden states, and a fixed learning rate of 1 in all experiments. We use a BPTT horizon of 35 in the PTB experiments, and an infinite BPTT horizon in all other experiments. |