Deep Multi-task Representation Learning: A Tensor Factorisation Approach
Authors: Yongxin Yang, Timothy M. Hospedales
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS |
| Researcher Affiliation | Academia | Yongxin Yang, Timothy M. Hospedales Queen Mary, University of London {yongxin.yang, t.hospedales}@qmul.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our method is implemented with Tensor Flow (Abadi et al., 2015). The code is released on Git Hub4. For DMTRL-Tucker, DMTRL-TT, and DMTRL-LAF, we need to assign the rank of each weight tensor. https://github.com/wOOL/DMTRL |
| Open Datasets | Yes | We use MNIST handwritten digits. The Adience Faces (Eidinger et al., 2014) is a large-scale face images dataset... We next consider the task of learning to recognise handwritten letters in multiple languages using the Omniglot (Lake et al., 2015) dataset. |
| Dataset Splits | Yes | We then use cross-validation to select among the three userdefined MTL architectures... Running 5-fold cross-validation on the train set to evaluate the architectures... |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | Our method is implemented with Tensor Flow (Abadi et al., 2015). |
| Experiment Setup | Yes | We use a modified Le Net (Le Cun et al., 1998) as the CNN architecture. The first convolutional layer has 32 filters of size 5 5, followed by 2 2 max pooling. The second convolutional layer has 64 filters of size 4 4, and again a 2 2 max pooling. After these two convolutional layers, two fully connected layers with 512 and 1 output(s) are placed sequentially. The convolutional and first FC layer use RELU f(x) = max(x, 0) activation function. We use hinge loss, ℓ(y) = max(0, 1 ˆy y)... |