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..
Correlative Channel-Aware Fusion for Multi-View Time Series Classification
Authors: Yue Bai, Lichen Wang, Zhiqiang Tao, Sheng Li, Yun Fu6714-6722
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on three real-world datasets demonstrate the superiority of our C2AF over the state-of-the-art methods. A detailed ablation study is also provided to illustrate the indispensability of each model component. |
| Researcher Affiliation | Academia | 1 Department of Electrical and Computer Engineering, Northeastern University, Boston, USA 2 Department of Computer Science and Engineering, Santa Clara University, Santa Clara, USA 3 Department of Computer Science, University of Georgia, Athens, USA |
| Pseudocode | Yes | Algorithm 1 The procedure of training C2AF algorithm. |
| Open Source Code | No | Code will be released at https://github.com/yueb17/C2AF |
| Open Datasets | Yes | EV-Action (Wang et al. 2019) is a multi-view human action dataset... NTU RGB+D (Shahroudy et al. 2016) is a large-scale dataset for multi-view action recognition... UCI Daily and Sports Activities (Asuncion and Newman 2007) is a multivariate time series dataset... |
| Dataset Splits | Yes | We choose the first 40 subjects for training and the rest 13 subjects for test. (EV-Action)... We use the cross-subject benchmark provided by the original dataset paper, which contains 40320 samples for training and 16560 samples for test. (NTU RGB+D) |
| Hardware Specification | No | Our model is implemented using Tensorflow with GPU acceleration. (No specific GPU model or other hardware details are provided.) |
| Software Dependencies | No | Our model is implemented using Tensorflow with GPU acceleration. (Tensorflow is mentioned, but no specific version number or other software dependencies with versions are listed.) |
| Experiment Setup | Yes | We set 128 as batch size. The Adam optimizer (Kingma and Ba 2014) is utilized for optimization and the learning rates are set as 0.0001 for all the view-specific and final classifiers synchronously. |