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..
Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks
Authors: Tim Whitaker, Darrell Whitley8638-8646
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We benchmark our approach against state of the art low-cost ensemble methods and display marked improvement in both accuracy and uncertainty estimation on CIFAR-10 and CIFAR-100. |
| Researcher Affiliation | Academia | Tim Whitaker, Darrell Whitley Department of Computer Science Colorado State University Fort Collins, CO 80525 EMAIL, EMAIL |
| Pseudocode | No | The paper contains mathematical formulas (e.g., for CD(A,B) and ηt) but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'Datasets and models are open source and linked in the appendix.' but does not provide a specific link or explicit statement for the availability of the authors' own source code for the methodology described. |
| Open Datasets | Yes | Datasets: We use the computer vision datasets, CIFAR10 and CIFAR-100 (Krizhevsky 2012). |
| Dataset Splits | No | The paper explicitly states the split into '50,000 training images and 10,000 testing images' for CIFAR, but does not provide specific details for a validation set split. |
| Hardware Specification | Yes | Hardware: All models are trained on a single Nvidia GTX-1080-Ti GPU. |
| Software Dependencies | No | The paper mentions software components like ADAM optimizer, but it does not specify version numbers for any software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | We use Stochastic Gradient Descent (SGD) with Nesterov momentum for our evaluation of random/anti-random pruning and constant/cyclic tuning schedule. We use an initial learning rate of η1 = 0.1 for 50% of the training budget which decays linearly to η2 = 0.001 at 90% of the training budget. The learning rate is kept constant at η2 = 0.001 for the final 10% of training. Children are tuned with either a constant learning rate of η = 0.01 for 5 epochs or with a one-cycle schedule that ramps up from η1 = 0.001 to η2 = 0.1 at 10% of tuning, then decaying to η3 = 1e 7 at the end of 5 epochs. For all other experiments, we use ADAM with a learning rate of η = 0.001. |