Learning and Inference in Hilbert Space with Quantum Graphical Models
Authors: Siddarth Srinivasan, Carlton Downey, Byron Boots
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results showing that HSE-HQMMs are competitive with state-of-the-art models like LSTMs and PSRNNs on several datasets, while also providing a nonparametric method for maintaining a probability distribution over continuous-valued features. 6 Experiments We use the following datasets in our experiments: |
| Researcher Affiliation | Academia | Siddarth Srinivasan College of Computing Georgia Tech Atlanta, GA 30332 sidsrini@gatech.edu Carlton Downey Department of Machine Learning Carnegie Mellon University Pittsburgh, PA 15213 cmdowney@cs.cmu.edu Byron Boots College of Computing Georgia Tech Atlanta, GA 30332 bboots@cc.gatech.edu |
| Pseudocode | Yes | Algorithm 1 Learning Algorithm using Two-Stage Regression for HSE-HQMMs |
| Open Source Code | No | Code will be made available at https://github.com/cmdowney/hsehqmm |
| Open Datasets | Yes | Penn Tree Bank (PTB) Marcus et al. [1993]. Swimmer Simulated swimmer robot from Open AI gym2. We collect 25 trajectories from a robot... Mocap Human Motion Capture Dataset. We collect 48 skeletal tracks from three human subjects... |
| Dataset Splits | No | We train a character-prediction model with a train/test split of 120780/124774 characters due to hardware limitations. We collect 25 trajectories from a robot... with a 20/5 train/test split. We collect 48 skeletal tracks from three human subjects with a 40/8 train/test split. Only train/test splits are specified, not validation. |
| Hardware Specification | No | No specific hardware details (like CPU/GPU models, memory, or specific cloud instances) used for the experiments are provided. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | No | Hyperparameters and other experimental details can be found in Appendix E. No specific hyperparameter values or detailed training configurations are provided in the main body of the paper. |