Crowdsourcing via Tensor Augmentation and Completion
Authors: Yao Zhou, Jingrui He
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on 6 real data sets demonstrate the superior performance of the proposed approach over state-of-the-art techniques. |
| Researcher Affiliation | Academia | Yao Zhou, Jingrui He Arizona State University, Tempe, Arizona yzhou174@asu.edu, jingrui.he@asu.edu |
| Pseudocode | Yes | Algorithm 1: PG-TAC |
| Open Source Code | No | The paper does not provide an explicit statement or link to its own open-source code. It mentions verifying baselines using an 'open source implementation provided by [Zhou et al., 2015]' but this is not for their described methodology. |
| Open Datasets | Yes | We also evaluate our methods with various other methods on six real world crowdsourcing data sets. [...] Temp data set [Snow et al., 2008]), RTE data set [Snow et al., 2008] and Spam data set [Zhou et al., 2015]. The multi-class labeling data sets include Dog data set [Zhou et al., 2012], Web data set [Zhou et al., 2012] and Age data set [Han et al., 2015]. |
| Dataset Splits | No | For each possible parameter pair on the searching grid, a subset of worker labels is randomly chosen from current data set without replacement. In practice, we empirically choose 90 percent of worker labels as a subset, run our methods, and evaluate the performance. Then we repeat the same procedure ten times for each possible parameter pair on the grid. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'open source implementation provided by [Zhou et al., 2015]' for verifying baselines, but it does not specify version numbers for any software dependencies relevant to its own methodology. |
| Experiment Setup | Yes | Eventually we can apply the grid search on two regularization parameters δl and γ, and the procedure is described as follows: all data sets we used are publicly available online and they all come with ground truth labels. |