Semi-supervised Sequence Classification through Change Point Detection
Authors: Nauman Ahad, Mark A. Davenport6574-6581
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide extensive synthetic simulations and show that the learned representations are better than those learned through an autoencoder and obtain improved results on simulations and human activity recognition datasets. |
| Researcher Affiliation | Academia | Nauman Ahad, Mark A. Davenport School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA {nahad3, mdav}@gatech.edu |
| Pseudocode | Yes | Algorithm 1 SSL via change point detection |
| Open Source Code | Yes | Code: https://github.com/nahad3/semi sup cp |
| Open Datasets | Yes | HCI: Gesture Recognition (Forster, Roggen, and Troster 2009) and WISDM: Activity Recognition (Kwapisz, Weiss, and Moore 2010) |
| Dataset Splits | Yes | In all synthetic simulations, we split the data in a 70/30 ratio where we use the larger split for training and the smaller split as a test dataset. We further split the training data in a ratio of 10/60/30. We use the smallest of these splits to obtain labeled data, the largest as unlabeled data for the semi-supervised setting, and the last split for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions types of networks and general libraries but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The neural network consists of 6 convolutional layers (or 3 temporal blocks as defined by (Bai, Kolter, and Koltun 2018)) followed by 1 linear layer. We use a RELU activation function after every convolutional layer. We use a 2-layer feedforward neural network followed by a softmax function to obtain a distribution over the different classes. Our loss function is constructed applying a hinge loss (with margin parameter ρ) to this KL divergence...Here, PL and PU denote the sets of sub-sequence pairs in PS Y PD formed from the labeled and unlabeled data, respectively, and λr is a tuning parameter which controls the influence of the unsupervised part of the loss function. Here, λC is a tuning parameter, LCE is the cross entropy loss, and LNE is the negative entropy loss. |