Light Multi-Segment Activation for Model Compression
Authors: Zhenhui Xu, Guolin Ke, Jia Zhang, Jiang Bian, Tie-Yan Liu6542-6549
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on state-of-the-art NN architectures over the real-world tasks demonstrate the effectiveness and extensibility of the LMA. |
| Researcher Affiliation | Collaboration | Zhenhui Xu,1 Guolin Ke,2 Jia Zhang,2 Jiang Bian,2 Tie-Yan Liu2 1Peking University, 2Microsoft Research |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | Yes | 2We released the code at: https://github.com/motefly/LMA |
| Open Datasets | Yes | we first evaluate our method on CIFAR-10 and CIFAR-100, both of which are wellknown image classification datasets. |
| Dataset Splits | Yes | Open NMT integration test dataset (Ope) consisting of 200K train sentences and 10K test sentences and WMT13 (Koehn 2005) dataset for a German-English translation task. |
| Hardware Specification | No | The paper mentions running experiments on GPU ('especially running on GPU'), but does not provide specific details such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | No | The paper refers to using 'Open NMT' and 'Moses project' but does not specify any software libraries or dependencies with version numbers required for reproducibility. |
| Experiment Setup | Yes | For fair comparisons, we set all the common parameters, including learning rate, batch size, hyper-parameters in distillation loss, etc., the same for all the baselines. |