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..
Teacher Assistant-Based Knowledge Distillation Extracting Multi-level Features on Single Channel Sleep EEG
Authors: Heng Liang, Yucheng Liu, Haichao Wang, Ziyu Jia
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method on two public sleep datasets (Sleep-EDF and ISRUC-III). Compared to the baseline methods, the results show that our knowledge distillation framework achieves state-of-the-art performance. Experimental results show that our knowledge distillation framework achieves SOTA performance compared to existing knowledge distillation methods. |
| Researcher Affiliation | Academia | 1 Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2 Tsinghua-Berkeley Shenzhen Institute, Shenzhen, China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https:// github.com/Hychao Wang/Sleep KD. |
| Open Datasets | Yes | We evaluate our method on two public datasets: ISRUC-III [Khalighi et al., 2016] and Sleep-EDF [Kemp et al., 2018]. |
| Dataset Splits | Yes | We split the datasets into the train, validation, and test sets by a ratio of 8:1:1 on Sleep-EDF and ISRUC-III separately. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. It only mentions implementing models with 'Tensor Flow'. |
| Software Dependencies | No | The paper mentions implementing models with 'Tensor Flow' but does not specify a version number or any other software dependencies with their versions, which is required for reproducible description. |
| Experiment Setup | Yes | We use Adam as the optimizer in each experiment. In experiments of the CNN framework, we choose Salient Sleep Net as a representative. The learning rate of Salient Sleep Net is 0.001. The number of training epochs is 60 and the batch size is 8. The weights are α = 0.3, β = 0.2, γ = 0.4 and δ = 0.1. In experiments of the CNN and RNN framework, we choose Deep Sleep Net as a representative. Deep Sleep Net has a learning rate of 0.00001. The number of training epochs is 200 for Sleep EDF, 300 for ISRUC-III and the batch size is 20. The weights are α = 1.0, β = 0.1 and γ = δ = 1.0. |