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..
Semi-supervised Sequence Classification through Change Point Detection
Authors: Nauman Ahad, Mark A. Davenport6574-6581
AAAI 2021 | Venue PDF | 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 EMAIL |
| 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. |