Self-Paced Multi-Task Learning
Authors: Changsheng Li, Junchi Yan, Fan Wei, Weishan Dong, Qingshan Liu, Hongyuan Zha
AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the toy and real-world datasets demonstrate the effectiveness of the proposed approach, compared to the state-of-the-arts. |
| Researcher Affiliation | Collaboration | Changsheng Li12, Junchi Yan12 , Fan Wei,3 Weishan Dong,2 Qingshan Liu,4 Hongyuan Zha51 1East China Normal University 2IBM Research China 3Stanford University 4Nanjing University of Info. Science & Tech 5Georgia Institute of Technology |
| Pseudocode | Yes | Algorithm 1 Self-Paced Multi-Task Learning (SPMTL) |
| Open Source Code | No | The paper does not contain any statement or link indicating that source code for their methodology is provided or publicly available. |
| Open Datasets | Yes | OHSUMED (Hersh et al. 1994) and Isolet1. The first one is an ordinal regression dataset... The second dataset is collected from 150 speakers... 1http://www.cad.zju.edu.cn/home/dengcai/Data/MLData.html |
| Dataset Splits | No | The paper mentions "randomly select the training instances from each task with different training ratios (5%, 10% and 15%) and use the rest of instances to form the testing set," but does not explicitly mention a distinct validation set or its split. |
| Hardware Specification | No | The paper does not provide specific hardware details such as CPU/GPU models, memory, or specific computing environments used for running experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | The regularization parameter α in (3) is used to control the complexity of the basis tasks. We find α = 100 works well on all the three datasets, and thus fix it to 100 throughout the experiments. The parameter β is tuned in the space [0.001, 0.01, 0.1, 1, 10, 100]. The parameters λ and γ influence how many tasks will be selected for training. Thus we initially set more than 20% tasks selected in the experiment. To determine the corresponding λ and γ, we adopt the grid search strategy based on the principle that larger λ and smaller γ can make more weights to be larger. After initialization, we increase λ and decrease γ to gradually involve hard tasks and instances at each iteration. |