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..
Tree-Structured Reinforcement Learning for Sequential Object Localization
Authors: Zequn Jie, Xiaodan Liang, Jiashi Feng, Xiaojie Jin, Wen Lu, Shuicheng Yan
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on PASCAL VOC 2007 and 2012 validate the effectiveness of the Tree-RL, which can achieve comparable recalls with current object proposal algorithms via much fewer candidate windows. |
| Researcher Affiliation | Academia | 1 National University of Singapore, Singapore 2 Carnegie Mellon University, USA |
| Pseudocode | No | The paper describes the proposed method in text and figures, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states that "The implementations are based on the publicly available Torch7 [25] platform", which refers to a third-party library used, not the authors' own source code for the proposed method. No direct link or explicit statement about releasing their code is provided. |
| Open Datasets | Yes | We train a deep Q-network on VOC 2007+2012 trainval set [7] for 25 epochs. |
| Dataset Splits | No | The paper mentions using the "VOC 2007+2012 trainval set" and a separate "testing set" but does not specify how the trainval set itself was split for training and validation, nor does it provide specific percentages or counts for a validation set. |
| Hardware Specification | Yes | The implementations are based on the publicly available Torch7 [25] platform on a single NVIDIA Ge Force Titan X GPU with 12GB memory. |
| Software Dependencies | No | The paper states that "The implementations are based on the publicly available Torch7 [25] platform", but it does not specify a version number for Torch7 or any other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | During ϵ-greedy training, ϵ is annealed linearly from 1 to 0.1 over the first 10 epochs. Then ϵ is fixed to 0.1 in the last 15 epochs. The discount factor γ is set to 0.9. We run each episode with maximal 50 steps during training. The replay memory size is set to 800,000, which contains about 1 epoch of transitions. The mini batch size in training is set to 64. |