ATD: Augmenting CP Tensor Decomposition by Self Supervision
Authors: Chaoqi Yang, Cheng Qian, Navjot Singh, Cao (Danica) Xiao, M Westover, Edgar Solomonik, Jimeng Sun
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our proposed ATD on multiple datasets. It can achieve 0.8% 2.5% accuracy gain over tensor-based baselines. Also, our ATD model shows comparable or better performance (e.g., up to 15% in accuracy) over self-supervised and autoencoder baselines while using less than 5% of learnable parameters of these baseline models. |
| Researcher Affiliation | Collaboration | 1University of Illinois Urbana-Champaign, 2IQVIA, 3Relativity, 4Massachusetts General Hospital, 5Harvard Medical School 1{chaoqiy2,navjot2,solomon2,jimeng}@illinois.edu, 2alextoqc@gmail.com, 3cao.xiao@relativity.com, 4,5mwestover@mgh.harvard.edu |
| Pseudocode | Yes | Algorithm 1: Alternating Least Squares |
| Open Source Code | Yes | We have released our data processing and codes in https://github.com/ycq091044/ATD. |
| Open Datasets | Yes | We use four real-world datasets: (i) Sleep-EDF (Kemp et al., 2000), (ii) human activity recognition (HAR) (Anguita et al., 2013), (iii) Physikalisch Technische Bundesanstalt large scale cardiology database (PTB-XL) (Alday et al., 2020), (iv) Massachusetts General Hospital (MGH) (Biswal et al., 2018) datasets. |
| Dataset Splits | Yes | All datasets are split into three disjoint sets (i.e., unlabeled, training and test) by subjects, while training and test sets have labels. Basic statistics are shown in Table 1. |
| Hardware Specification | Yes | The experiments are implemented by Python 3.8.5, Torch 1.8.0+cu111 on a Linux workstation with 256 GB memory, 32 core CPUs (3.70 GHz, 128 MB cache), two RTX 3090 GPUs (24 GB memory each). |
| Software Dependencies | Yes | The experiments are implemented by Python 3.8.5, Torch 1.8.0+cu111 on a Linux workstation with 256 GB memory, 32 core CPUs (3.70 GHz, 128 MB cache), two RTX 3090 GPUs (24 GB memory each). |
| Experiment Setup | Yes | All models (baselines and our ATD) use the same augmentation techniques: (a) jittering, (b) bandpass filtering, and (c) 3D position rotation. We provide an ablation study on the augmentation methods in Appendix C.5. ... For tensor based models, we use R = 32 and implement the pipeline in CUDA manually, instead of using torch-autograd. |