Adversarial Sequence Tagging
Authors: Jia Li, Kaiser Asif, Hong Wang, Brian D. Ziebart, Tanya Berger-Wolf
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we demonstrate the effectiveness of our proposed AST model. Table 3: Per-variable accuracy for the three approaches on different datasets. Table 4 shows the amount of time required to make predictions for all of the testing sequences. |
| Researcher Affiliation | Academia | Department of Computer Science, University of Illinois at Chicago, Chicago, IL {jli213, kasif2,hwang207, bziebart, tanyabw}@uic.edu |
| Pseudocode | Yes | Algorithm 1 Single Oracle Game Solver. Algorithm 2 Parameter Estimation Algorithm. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the methodology described in the paper. |
| Open Datasets | Yes | Human Activity Recognition Dataset [Reyes-Ortiz et al., 2015]. Baboon Activity Recognition Dataset [Strandburg-Peshkin et al., 2015; Crofoot et al., 2015]. FAQ Segmentation Dataset [Mc Callum et al., 2000]. |
| Dataset Splits | Yes | We selected the regularization weights using a validation set (approximately 10% of the data). We use a validation set of 10% of the data for selecting the parameter c which controls the trade-off between slack and the magnitude of the weights vectors, and default parameters for the remaining settings. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like LBFGS, SVM hmm, SVM light, and Gurobi, but does not provide specific version numbers for these software components to ensure reproducibility. |
| Experiment Setup | No | The paper mentions using stochastic gradient descent and selecting regularization weights and a parameter 'c' using a validation set, but it does not provide specific values for hyperparameters or detailed training configurations (e.g., learning rate, batch size, number of epochs). |