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..
Exploiting Submodular Value Functions for Faster Dynamic Sensor Selection
Authors: Yash Satsangi, Shimon Whiteson, Frans Oliehoek
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a realworld dataset from a multi-camera tracking system in a shopping mall show it achieves similar performance to existing methods but incurs only a fraction of the computational cost, leading to much better scalability in the number of cameras. |
| Researcher Affiliation | Academia | Yash Satsangi and Shimon Whiteson Informatics Institute, University of Amsterdam Amsterdam, The Netherlands EMAIL Frans A. Oliehoek Informatics Institute, University of Amsterdam Dept. of CS, University of Liverpool EMAIL |
| Pseudocode | Yes | Algorithm 1 greedy-argmax(F, X, K) |
| Open Source Code | No | The paper does not mention releasing source code or provide any links for its implementation. |
| Open Datasets | Yes | The problem was extracted from a real-world dataset collected in a shopping mall (Bouma et al. 2013). |
| Dataset Splits | No | The paper does not specify validation splits or proportions (e.g., 'X% for validation'). |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameters, training configurations, or system-level settings. |