Learning Logistic Circuits
Authors: Yitao Liang, Guy Van den Broeck4277-4286
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 {yliang, guyvdb}@cs.ucla.edu |
| 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 |