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..
DIVISION: Memory Efficient Training via Dual Activation Precision
Authors: Guanchu Wang, Zirui Liu, Zhimeng Jiang, Ninghao Liu, Na Zou, Xia Hu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results show DIVISION has better comprehensive performance than stateof-the-art methods, including over 10 compression of activation maps and competitive training throughput, without loss of model accuracy. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Rice University 2Department of Computer Science and Engineering, Texas A&M University 3Department of Computer Science, University of Georgia 4Department of Engineering Technology, Texas A&M University. |
| Pseudocode | Yes | Algorithm 1 Mini-batch updating of DIVISION" and "Algorithms 2, 3, 4 and 5 to compresse the activation map of a Max-Pooling layer, Average Pooling layer, Relu activation and Dropout operator, respectively. |
| Open Source Code | Yes | The source code is available at https://github. com/guanchuwang/division. |
| Open Datasets | Yes | We consider CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009) and Image Net (Deng et al., 2009) datasets in our experiments. |
| Dataset Splits | Yes | Our reproduced validating accuracy on the Image Net dataset is consistent with the official results of torchvision" and "We consider CIFAR-10, CIFAR-100 (Krizhevsky et al., 2009) and Image Net (Deng et al., 2009) datasets in our experiments. |
| Hardware Specification | Yes | GPU model Nvidia-RTX3090 |
| Software Dependencies | Yes | CUDA Version 12.0 |
| Experiment Setup | Yes | Table 10: Hyper-parameter setting." which includes "Epoch 100", "Batch-size 256", "Initial LR 0.1", "LR scheduler Cos LR", "Weight-decay 0.0005", "Optimizer SGD", "Momentum 0.9", "Block-size B 8", "Bit-width Q 2". |