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..
MetaRLEC: Meta-Reinforcement Learning for Discovery of Brain Effective Connectivity
Authors: Zuozhen Zhang, Junzhong Ji, Jinduo Liu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct comprehensive experiments on both simulated and real-world data to demonstrate the efficacy of our proposed method.Systematic experiments conducted on both simulated and real f MRI datasets demonstrate that the proposed method surpasses several state-of-the-art approaches in its performance on small-sample f MRI data. |
| Researcher Affiliation | Academia | Zuozhen Zhang, Junzhong Ji, Jinduo Liu * Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Meta RLEC Input: Original f MRI time-series data. Output: Brain EC network. |
| Open Source Code | Yes | The code is available at https://github.com/layzoom/Meta RLEC. |
| Open Datasets | Yes | The benchmark simulation datasets 1 we used are supported by Smith et al. (Smith et al. 2011), which are generated by dynamic causal models (DCM).1https://www.fmrib.ox.ac.uk/datasets/netsim/index.html 2https://github.com/shahpreya/MTlnet |
| Dataset Splits | Yes | dtrn is for training and dval is for validation of online learning.Algorithm 1: ...7: Sample training batch dtrn from X; ... 14: Sample testing batch dval from X; |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments were mentioned in the paper. |
| Software Dependencies | No | The paper describes model components and mathematical functions but does not provide specific software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | where λ ≥ 0 is a parameter that controls the sparsity of brain EC networks and A(G) is the sparse penalty function as A(G) = G 1.where η denotes the learning rate.The parameters of the algorithms under comparison are selected according to the existing literature and we fine-tune 10 subjects to select the optimal parameters. |