Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
GUST: Combinatorial Generalization by Unsupervised Grouping with Neuronal Coherence
Authors: Hao Zheng, Hui Lin, Rong Zhao
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,128 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,269 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate and analyze the model on synthetic datasets. |
| Researcher Affiliation | Collaboration | Hao Zheng Hui Lin Rong Zhao Center for Brain-Inspired Computing Research, Optical Memory National Engineering Research Center, Tsinghua University China Electronics Technology HIK Group Co. Joint Research Center for Brain-Inspired Computing, IDG / Mc Govern Institute for Brain Research at Tsinghua University, Department of Precision Instrument, Tsinghua University, Beijing 100084, China. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks labeled as such. |
| Open Source Code | Yes | The code is publicly available at: https://github.com/monstersecond/gust. |
| Open Datasets | No | synthetic images composed of multiple Shapes[45] are generated for evaluation. |
| Dataset Splits | No | The paper mentions training on '54000 images' and evaluating AMI/Syn Score 'averaged over all testing images' during training, but does not specify explicit training/validation/test splits with percentages or counts, or refer to predefined benchmark splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | τ2 = 10. The GUST is simulated 3-times longer than training... We add salt&pepper noise to the input... |