Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification
Authors: Junru Chen, Tianyu Cao, Jing Xu, Jiahe Li, Zhilong Chen, Tao Xiao, YANG YANG
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments across multiple datasets validate the effectiveness of Con4m in handling segmented TSC tasks on MVD. |
| Researcher Affiliation | Collaboration | Junru Chen Zhejiang University jrchen_cali@zju.edu.cn Tianyu Cao Zhejiang University ty.cao@zju.edu.cn Jing Xu State Grid Power Supply Co. Ltd. ltxu1111@gmail.com Jiahe Li Zhejiang University jiaheli@zju.edu.cn Zhilong Chen Zhejiang University zhilongchen@zju.edu.cn Tao Xiao State Grid Power Supply Co. Ltd. xtxjtu@163.com Yang Yang Zhejiang University yangya@zju.edu.cn |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It describes the method in text and with diagrams. |
| Open Source Code | Yes | The source code is available at https://github.com/MrNobodyCali/Con4m. |
| Open Datasets | Yes | In this work, we use three public [31, 7, 37] and one private MVD data to measure the performance of models. Specifically, the Tufts f NIRS to Mental Workload [31] data (f NIRS)... The HHAR (Heterogeneity Human Activity Recognition) dataset [7]... The Sleep EDF [37] data (Sleep)... |
| Dataset Splits | Yes | We use cross-validation [39] to evaluate the model s generalization ability by partitioning the subjects in the data into non-overlapping subsets for training and testing. As shown in Table 1, for f NIRS and SEEG, we divide the subjects into 4 groups and follow the 2 training-1 validation-1 testing (2-1-1) setting to conduct experiments. We divide the HHAR and Sleep datasets into 3 groups and follow the 1-1-1 experimental setting. |
| Hardware Specification | Yes | And the model is trained on a workstation (Ubuntu system 20.04.5) with 2 CPUs (AMD EPYC 7H12 64-Core Processor) and 8 GPUs (NVIDIA Ge Force RTX 3090). |
| Software Dependencies | Yes | We build our model using Py Torch 2.0.0 [52] with CUDA 11.8. |
| Experiment Setup | Yes | We set d=128 and the dimension of intermediate representations in FFN module as 256 for all experiments. The number of heads and dropout rate are set as 8 and 0.1 respectively... The model is optimized using Adam optimizer [38] with a learning rate of 1e-3 and weight decay of 1e-4, and the batch size is set as 64. |