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..
CLIP-MUSED: CLIP-Guided Multi-Subject Visual Neural Information Semantic Decoding
Authors: Qiongyi Zhou, Changde Du, Shengpei Wang, Huiguang He
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed method outperforms single-subject decoding methods and achieves stateof-the-art performance among the existing multi-subject methods on two f MRI datasets. |
| Researcher Affiliation | Academia | 1Laboratory of Brain Atlas and Brain-Inspired Intelligence, State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences 2University of Chinese Academy of Sciences |
| Pseudocode | No | The paper provides mathematical formulations and architectural diagrams but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/CLIP-MUSED/CLIPMUSED. |
| Open Datasets | Yes | HCP (Glasser et al., 2013; Van Essen et al., 2012): This dataset is a part of the Human Connectome Project (HCP), containing BOLD signals from 158 subjects. |
| Dataset Splits | Yes | For the single-subject decoding task, the training, validation, and test sets consist of 2000, 265, and 699 samples, respectively. |
| Hardware Specification | Yes | The models converge after approximately three hours of training on one NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions 'Adam' as an optimizer and 'Brain IAK' for a baseline method but does not provide specific version numbers for software dependencies used in their own implementation, such as Python, PyTorch, or other libraries. |
| Experiment Setup | Yes | The learning rate is set to 0.001, and the batch size is 64, and the optimizer is Adam. We find the optimal values for the hyperparameters λ , λhlv, and λllv by grid-search within the range of [0.001, 0.01, 0.1] and the best values are λ = 0.001, λhlv = 0.001, λllv = 0.1. |