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.
Learning Logistic Circuits
Authors: Yitao Liang, Guy Van den Broeck4277-4286
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,192 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,493 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 | On MNIST and Fashion datasets, our learning algorithm outperforms neural networks that have an order of magnitude more parameters. We run experiments on standard image classification benchmarks (MNIST and Fashion) and achieve accuracy higher than much larger MLPs and even CNNs with an order of magnitude more parameters. |
| Researcher Affiliation | Academia | Yitao Liang, Guy Van den Broeck Computer Science Department University of California, Los Angeles EMAIL |
| Pseudocode | Yes | Algorithm 1: Node probabilities from a real-valued sample x. Algorithm 2: Features x from a real-valued sample x. |
| Open Source Code | Yes | Open-source code and experiments are available at https://github.com/UCLA-StarAI/LogisticCircuit. |
| Open Datasets | Yes | We choose MNIST and Fashion5 as our testbeds. (Xiao, Rasul, and Vollgraf 2017) (Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms.) |
| Dataset Splits | Yes | The learned structure with the highest F1 score on validation after 48 hours of running is used for evaluation. |
| Hardware Specification | No | All experiments are run on single CPUs. (This does not provide specific model numbers or detailed specifications.) |
| Software Dependencies | No | No specific version numbers for software dependencies (e.g., Python, PyTorch, TensorFlow, etc.) are provided. |
| Experiment Setup | Yes | All experiments start with a predefined initial structure; we defer its details to Appendix D. The learned structure with the highest F1 score on validation after 48 hours of running is used for evaluation. Appendix D. Initial Structure. Appendix E Details of Existing Classification Models |