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..
BATUDE: Budget-Aware Neural Network Compression Based on Tucker Decomposition
Authors: Miao Yin, Huy Phan, Xiao Zang, Siyu Liao, Bo Yuan8874-8882
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on Image Net dataset show that our method enjoys 0.33% top-5 higher accuracy with 2.52 less computational cost as compared to the uncompressed Res Net-18 model. |
| Researcher Affiliation | Collaboration | Miao Yin1*, Huy Phan1, Xiao Zang1, Siyu Liao2 , Bo Yuan1 1Department of Electrical and Computer Engineering, Rutgers University, 2Amazon EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Budget-Aware Training with BC-ADL |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We evaluate the proposed BATUDE for various popular CNN models on CIFAR-10, CIFAR-100 and Image Net datasets. |
| Dataset Splits | No | The paper mentions using CIFAR-10, CIFAR-100, and ImageNet datasets, but does not provide specific details on the training, validation, and test splits (e.g., percentages or sample counts). |
| 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 | The paper mentions 'Training optimizer is set as momentum SGD' but does not specify software versions for libraries, frameworks, or programming languages used. |
| Experiment Setup | Yes | Learning rate is set as 0.1 and 0.01 for CIFAR and Image Net dataset, respectively. The learning rate is multiplied by a factor of 0.1 every 30 epochs. Training optimizer is set as momentum SGD. For other hyper-parameters, batch size, weight decay and Ξ» are set as 256, 0.0001, and 0.001. Also, the compressed Tucker-format models are ο¬ne-tuned with learning rate 0.005 and 0.001 on CIFAR and Image Net dataset, respectively. |