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 [1].
Reinforced Cross-Domain Knowledge Distillation on Time Series Data
Authors: QING XU, Min Wu, Xiaoli Li, Kezhi Mao, Zhenghua Chen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experimental results and analyses on four public time series datasets demonstrate the effectiveness of our proposed method over other state-of-the-art benchmarks. |
| Researcher Affiliation | Academia | Qing Xu Institute for Infocomm Research A*STAR, Singapore Nanyang Technological University EMAIL Min Wu Institute for Infocomm Research A*STAR, Singapore EMAIL Xiaoli Li Institute for Infocomm Research, A*STAR, Singapore A*STAR Centre for Frontier AI Research, Singapore EMAIL Kezhi Mao Nanyang Technological University EMAIL Zhenghua Chen Institute for Infocomm Research, A*STAR, Singapore A*STAR Centre for Frontier AI Research, Singapore EMAIL |
| Pseudocode | Yes | Algorithm 1 Proposed RCD-KD |
| Open Source Code | Yes | Our source code is available at https://github.com/xuqing88/Reinforced-Cross-Domain-Knowledge-Distillationon-Time-Series-Data. |
| Open Datasets | Yes | To evaluate our method, extensive experiments are conducted on four public datasets across three different tasks, namely human activity recognition, rolling bearing fault diagnosis and sleep stage classification. To be specific, the first dataset is called human activity recognition (HAR) [29]... The second evaluation dataset is Heterogeneity human activity recognition (HHAR) [31]... The third dataset is rolling bearing fault diagnosis (FD) [33]... The last evaluation dataset is sleep stage classification (SSC) dataset [35]... |
| Dataset Splits | No | Following standard UDA setup, we consider data from two domains: a labeled source domain DL src = {(xi s, yi s)}ns i=1 and an unlabeled target domain DU tgt = {xi t}nt i=1 which shares the same label space as source domain but has different data distributions. |
| Hardware Specification | Yes | Specifically, we measure the training time for our proposed method and other benchmarks with a NVIDIA 2080Ti GPU. |
| Software Dependencies | No | The paper describes its implementation details, including network architectures and loss functions, but does not specify versions of programming languages or libraries (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | For the proposed RL-based sample selection module, we set γ = 0.9 and δ = 0.999 following [25] in Eq. (5) and (6), respectively. We set N = 10 to calculate teacher s entropy for the reward function and K = 5 for the episodes to generate historical experience... For α1, α2 in Eq. (3) and λ, τ in Eq. (10), we use the grid search and set α1 = 0.2, α2 = 1.8, λ = 1.0, τ = 2 for all experiments. |