Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Online Variational Sequential Monte Carlo
Authors: Alessandro Mastrototaro, Jimmy Olsson
ICML 2024 | Venue PDF | LLM Run Details | Input Tokens: 32,439 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,847 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we illustrate OVSMC numerically on a number of classical SSM and more complex generative models, for which the method exhibits fast parameter learning and efficient adaptation of the particle proposal kernel. In the same numerical study, we also show that OVSMC is a strong challenger of VSMC on batch problems. |
| Researcher Affiliation | Academia | Alessandro Mastrototaro 1 Jimmy Olsson 1 1Department of Mathematics, KTH Royal Institute of Technology , Stockholm, Sweden. |
| Pseudocode | Yes | A pseudocode for our algorithm, which we refer to as online variational SMC (OVSMC), is displayed in Algorithm 2 |
| Open Source Code | Yes | The Python code may be found at https://bitbucket. org/amastrot/ovsmc. |
| Open Datasets | No | The paper describes generating its own data for experiments (e.g., 'generated data under A = 0.8, B = 1 and Su = 0.5' and 'produced a long and partially observable video sequence'), but does not provide concrete access information (link, DOI, formal citation) to make these datasets publicly available or identify them as open datasets. |
| Dataset Splits | No | The paper describes processing data in an online fashion but does not specify exact train/validation/test splits, percentages, or sample counts for reproducibility. |
| Hardware Specification | Yes | All the experiments are run on an Apple Mac Book Pro M1 2020, memory 8GB. |
| Software Dependencies | No | Stochastic gradients are passed to the ADAM optimizer (Kingma & Ba, 2015) in Tensorflow 2. |
| Experiment Setup | Yes | We consider two cases for Sv {0.2, 1.2} corresponding to informative and more non-informative observations, respectively. ... We let rλ( | xt, yt+1) be N(µλ(xt, yt+1), σ2 λ(xt, yt+1)), where µλ and σ2 λ are two distinct neural networks with one dense hidden layer having three and two nodes, respectively, and relu activation functions. |