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..
PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions
Authors: Zhengyang Shen, Lingshen He, Zhouchen Lin, Jinwen Ma
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on rotated MNIST and natural image classification show that PDO-e Convs perform competitively yet use parameters much more efficiently. |
| Researcher Affiliation | Academia | 1School of Mathematical Sciences and LMAM, Peking University, Beijing 100871 2Key Lab. of Machine Perception (Mo E), School of EECS, Peking University, Beijing 100871. Correspondence to: Zhouchen Lin <EMAIL>, Jinwen Ma <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | The most commonly used dataset for validating rotationequivariant algorithms is MNIST-rot-12k (Larochelle et al., 2007). ... CIFAR-10 (C10) and CIFAR-100 (C100) (Krizhevsky & Hinton, 2009) |
| Dataset Splits | Yes | We randomly select 2,000 training images as a validation set. ... The training and the test sets contain 50,000 and 10,000 images, respectively. We randomly select 5,000 training images as a validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | all the experiments are implemented using Tensorflow. |
| Experiment Setup | Yes | The model is trained using the Adam algorithm (Kingma & Ba, 2015) with a weight decay of 0.01. ... We train using batch size 128 for 200 epochs. The initial learning rate is set to 0.001 and is divided by 10 at 50% and 75% of the total number of training epochs. We set the dropout rate as 0.2. |