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..
A Partially-Supervised Reinforcement Learning Framework for Visual Active Search
Authors: Anindya Sarkar, Nathan Jacobs, Yevgeniy Vorobeychik
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate that the proposed representation and meta-learning frameworks significantly outperform state of the art in visual active search on several problem domains. |
| Researcher Affiliation | Academia | Anindya Sarkar Nathan Jacobs Yevgeniy Vorobeychik EMAIL, Department of Computer Science and Engineering Washington University in St. Louis |
| Pseudocode | Yes | Algorithm 1 The PSVAS algorithm. |
| Open Source Code | Yes | Our code is publicly available at this link. |
| Open Datasets | Yes | We evaluate the proposed approach using two datasets: x View [16] and DOTA [17]. |
| Dataset Splits | Yes | We use 67% and 33% of the large satellite images to train and test the policy network respectively. |
| Hardware Specification | Yes | We use 1 NVidia A100 and 3 Ge Force GTX 1080Ti GPU servers for all our experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'Res Net-34' but does not provide specific version numbers for programming languages or libraries such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We use a learning rate of 10 4, batch size of 16, number of training epochs 200, and the Adam optimizer to train the policy network in all experimental settings. |