Predictive State Recurrent Neural Networks
Authors: Carlton Downey, Ahmed Hefny, Byron Boots, Geoffrey J. Gordon, Boyue Li
NeurIPS 2017 | Conference 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 cmdowney@cs.cmu.edu Ahmed Hefny Carnegie Mellon University Pittsburgh, PA, 15213 ahefny@cs.cmu.edu Boyue Li Carnegie Mellon University Pittsburgh, PA, 15213 boyue@cs.cmu.edu Byron Boots Georgia Tech Atlanta, GA, 30332 bboots@cc.gatech.edu Geoff Gordon Carnegie Mellon University Pittsburgh, PA, 15213 ggordon@cs.cmu.edu |
| 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. |