Adaptive Adversarial Multi-task Representation Learning
Authors: Yuren Mao, Weiwei Liu, Xuemin Lin
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further conduct extensive experiments to back up our theoretical analysis and validate the superiority of our proposed algorithm. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, University of New South Wales, Australia. 2School of Computer Science, Wuhan University, China. |
| Pseudocode | Yes | Algorithm 1 Adaptive Adversarial MTRL |
| Open Source Code | Yes | The code can be found in the Supplementary Materials. |
| Open Datasets | Yes | The training/testing/validation partition is randomly split into 70% training, 10% testing and 20% validation. The training/testing/validation partition is randomly split into 60% training, 20% testing and 20% validation. |
| Dataset Splits | Yes | The training/testing/validation partition is randomly split into 70% training, 10% testing and 20% validation. The training/testing/validation partition is randomly split into 60% training, 20% testing and 20% validation. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The implementation is based on Py Torch (Paszke et al., 2019). |
| Experiment Setup | Yes | We train the deep AAMTRL network model with Algorithm 1 settings λ0 = 1, r0 = 10 and rk+1 = rk + 2; here, R0 is a matrix of ones. We use the Adam optimizer (Kingma & Ba, 2015) and train 600 epochs for sentiment analysis and 1200 epochs for topic classification, The batch size is 256 for both sentiment analysis and topic classification. We use dropout with probability of 0.5 for all task-specific output modules. For all experiments, we search over the set {1e 4, 5e 4, 1e 3, 5e 3, 1e 2, 5e 2} of learning rates and choose the model with the highest validation accuracy. |