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..
Gaussian Mixture Convolution Networks
Authors: Adam Celarek, Pedro Hermosilla, Bernhard Kerbl, Timo Ropinski, Michael Wimmer
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A thorough evaluation of GMCNs, including ablation studies. and To evaluate our architecture, we use the proposed GMCN architecture to train classification networks on a series of well-known tasks. |
| Researcher Affiliation | Academia | TU Wien Ulm University |
| Pseudocode | No | The paper describes methods and equations, but does not include explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | The source code for a proof-of-concept implementation, instructions, and benchmark datasets are provided in our Git Hub repository (https://github.com/cg-tuwien/ Gaussian-Mixture-Convolution-Networks). |
| Open Datasets | Yes | The MNIST data set (Lecun et al., 1998) and Model Net10 (Zhirong Wu et al., 2015) |
| Dataset Splits | Yes | We trained our GMCN architecture using the standard train/test splits by fitting 64 Gaussians to each image and processing them with our model. and We trained our network on Model Net10 using the standard train/test splits without data augmentation. |
| Hardware Specification | Yes | We used an NVIDIA Ge Force 2080Ti for training. |
| Software Dependencies | No | The paper mentions software like Adam optimizer, PyTorch, Tensor Board, and autodiff C++ library (Leal, 2018), but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | All models are trained using the Adam optimizer, with an initial learning rate of 0.001. The learning rate is reduced by a scheduler once the accuracy plateaus. Moreover, we apply weight decay scaled by 0.1 of the learning rate to avoid overfitting, as outlined in Section 4.2. |