Monte Carlo Filtering Objectives
Authors: Shuangshuang Chen, Sihao Ding, Yiannis Karayiannidis, Mårten Björkman
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that the proposed MCFOs and gradient estimations lead to efficient and stable model learning, and learned generative models well explain data and importance proposals are more sample efficient on various kinds of time series data. We seek to evaluate our method in experiments by answering: 1) what is the side-effect of ignoring the high variance term as proposed in earlier methods; 2) do the gradient estimates of MCFOs reduce the variance without the cost of additional bias; 3) how does the number of samples affect the learning of generative models and how sample efficient are the learned proposal models? We evaluate two instances of MCFOs, MCFO-SMC and MCFO-PIMH, using SMC and PIMH respectively to learn generative and proposal models on LGSSM, non-Gaussian, nonlinear, high dimensional SSMs of video sequences, and non-Markovian polyphonic music sequences. |
| Researcher Affiliation | Collaboration | 1 Royal Institute of Technology, Stockholm, Sweden 2 AI Lab, Volvo Car Corporation 3 Chalmers University of Technology, Gothenburg, Sweden |
| Pseudocode | No | The paper does not contain any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The implementation of our algorithms and experiments are available at https://github.com/ssajj1212/MCFO. |
| Open Datasets | Yes | We simulate 1000 video sequences of a single pendulum system in gym... We train VRNN models with MCFO-SMC and MCFO-PIMH on four polyphonic music datasets [Boulanger et al., 2012]. |
| Dataset Splits | No | For LGSSMs, we generate 5000 trajectories... of which 4000 are for training and rest for testing. For video sequences, we simulate 1000 video sequences... out of which 500 are used for testing. The paper specifies training and testing splits but does not explicitly mention a validation split or cross-validation for reproduction. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or memory specifications). |
| Software Dependencies | No | The paper describes the models and algorithms used but does not specify software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | The latent dimension is set to 3, and optimizers and model definitions are the same for all methods; see [Chen et al., 2021, Appendix E]. we generate 5000 trajectories by LGSSM in (9) with θ1 = 0.9, θ2 = 1.2. |