Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Online Variational Filtering and Parameter Learning
Authors: Andrew Campbell, Yuyang Shi, Thomas Rainforth, Arnaud Doucet
NeurIPS 2021 | Venue PDF | 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 EMAIL |
| 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. |