Lifted Filtering via Exchangeable Decomposition
Authors: Stefan Lüdtke, Max Schröder, Sebastian Bader, Kristian Kersting, Thomas Kirste
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our approach is demonstrated on two multiagent activity recognition tasks in Section 5. ... We compare the the cardinality p(L) (handled by Li Ma) and p(S) which is an indicator of space and time complexity. ... Figures 1 and 2 show the number of states necessary to represent the posterior p(St) over time. ... Figure 3 shows the mean number of states occurring during inference for the second scenario, for different numbers of agents. |
| Researcher Affiliation | Academia | 1Institute of Computer Science, University of Rostock, Germany 2Computer Science Department and Centre for Cognitive Science, TU Darmstadt, Germany |
| Pseudocode | Yes | Algorithm 1 For lifted state l, split the property q K with label d D, distributed according to urn u. |
| Open Source Code | No | The paper does not provide any links to open-source code for the described methodology or state that the code is available. |
| Open Datasets | Yes | The dataset is available at [Kasparick and Kr uger, 2013]. |
| Dataset Splits | No | The paper mentions using 'simulated sensor data' and 'real sensor data' but does not provide any specific details regarding train, validation, or test splits (e.g., percentages, sample counts, or cross-validation folds). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers used in the experiments. |
| Experiment Setup | No | The paper describes the general domains and data used (e.g., 'two multiagent activity recognition tasks', 'simulated sensor data', 'real sensor data') but does not specify concrete experimental setup details such as hyperparameters, training configurations, or model initialization settings. |