Semi-Supervised Learning on Data Streams via Temporal Label Propagation
Authors: Tal Wagner, Sudipto Guha, Shiva Kasiviswanathan, Nina Mishra
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on real datasets validate that the algorithm can quickly and accurately classify points on a stream with a small quantity of labeled examples. We evaluate our solution on several real datasets. |
| Researcher Affiliation | Collaboration | 1CSAIL, MIT. Work done during an internship at Amazon. 2Amazon. Correspondence to: Tal Wagner <talw@mit.edu>. |
| Pseudocode | Yes | Algorithm TLP : Temporal Label Propagation |
| Open Source Code | No | The paper does not provide a link or explicit statement about the availability of the source code for the described methodology. |
| Open Datasets | Yes | (a) Incart-ECG (Goldberger et al., 2000): Dataset of ECG timeseries from Physio Net bank (b) Daphnet-Gait (Bachlin et al., 2010): Annotated readings of 9 accelerometer sensors (c) Caltech10-101 (Fei-Fei et al., 2006): Caltech-101 dataset consists of images annotated by 101 object classes (d) Cam Vid-Car (Brostow et al., 2009) (Cambridge-driving Labeled Video Database) |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset splits (e.g., percentages, sample counts, or explicit references to standard splits) needed to reproduce the data partitioning. |
| Hardware Specification | Yes | All experiments were performed on a 3.1 GHz Intel Core i7 machine with 16GB RAM. |
| Software Dependencies | No | The paper mentions using "standard RBF similarity" but does not specify any software libraries, frameworks, or solvers with version numbers that would be necessary for reproduction. |
| Experiment Setup | Yes | We set σ = 0.1 for Incart-ECG, Daphnet-Gait, and Cam Vid and σ = 10 for Caltech10-101. |