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..
Lifted Proximal Operator Machines
Authors: Jia Li, Cong Fang, Zhouchen Lin4181-4188
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on MNIST and CIFAR-10 datasets testify to the advantages of LPOM. We implement LPOM on fully connected DNNs and test it on benchmark datasets, MNIST and CIFAR-10, and obtain satisfactory results. |
| Researcher Affiliation | Academia | Jia Li, Cong Fang, Zhouchen Lin Key Laboratory of Machine Perception (MOE), School of EECS, Peking University, P. R. China EMAIL; EMAIL; EMAIL |
| Pseudocode | Yes | Algorithm 1 Solving LPOM; Algorithm 2 Solving (32). |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-sourcing the code for LPOM. |
| Open Datasets | Yes | Experiments on MNIST and CIFAR-10 datasets testify to the advantages of LPOM. For the MNIST dataset, we use 28 28 = 784 raw pixels as the inputs. It includes 60,000 training images and 10,000 test images. http://yann.lecun.com/exdb/mnist/ |
| Dataset Splits | No | For the MNIST dataset, we use 28 28 = 784 raw pixels as the inputs. It includes 60,000 training images and 10,000 test images. The paper mentions training and testing, but no explicit validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | We implement LPOM with MATLAB without optimizing the code. We use the SGD based solver in Caffe (Jia et al. 2014). No version numbers are provided for MATLAB or Caffe. |
| Experiment Setup | Yes | We run LPOM and SGD for 100 epochs with a fixed batch size 100. For LPOM, we simply set µi =20 in (18). For LPOM, we set µi = 100 in (18). For LPOM, we set µi = 20 for all the networks. |