Online Variational Filtering and Parameter Learning
Authors: Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the performance of this methodology across several examples, including high-dimensional SSMs and sequential Variational Auto-Encoders. |
| Researcher Affiliation | Academia | Andrew Campbell Yuyang Shi Tom Rainforth Arnaud Doucet Department of Statistics, University of Oxford, UK {campbell, yshi, rainforth, doucet}@stats.ox.ac.uk |
| Pseudocode | Yes | Algorithm 1: Online Variational Filtering and Parameter Learning. |
| Open Source Code | Yes | Code available at https://github.com/andrew-cr/online_var_fil |
| Open Datasets | Yes | We perform this experiment on a video sequence from a Deep Mind Lab environment [5] (GNU GPL license). |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages or sample counts) for training, validation, or testing. |
| Hardware Specification | No | The paper discusses computational cost and high-dimensional models but does not specify any hardware details like GPU models, CPU types, or memory used for the experiments. |
| Software Dependencies | No | The paper mentions using MLPs and KRR, but it does not specify any software versions (e.g., Python, PyTorch, TensorFlow, scikit-learn) required to reproduce the experiments. |
| Experiment Setup | Yes | For dx = dy = 10, we first demonstrate accurate state inference by learning φt at each time step whilst holding θ fixed at the true value. We represent ˆTt(xt) non-parametrically using KRR. Full details for all experiments are given in Appendix B.4. ... We reproduce the Chaotic Recurrent Neural Network (CRNN) example in [44], but with state dimension dx = 5, 20, and 100. ... We let qφt t (xt 1|xt) = N(xt 1; MLPφt t (xt), diag( σ2 t )) and qφt t (xt) = N xt; µt, diag(σ2 t ) where we use a 1-layer Multi-Layer Perceptron (MLP) with 100 neurons for each qφt t (xt 1|xt). ... where dx = 32, NNf θ is a residual MLP and NNg a convolutional neural network. ... We use the same qφt t parameterization as for the CRNN but with a 2 hidden layer MLP with 64 neurons. |