Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach
Authors: Malik Tiomoko, Hafiz Tiomoko Ali, Romain Couillet
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This article provides theoretical insights into the inner workings of multi-task and transfer learning methods... Experiments on popular datasets demonstrate that our improved MTL LS-SVM method is computationally-efficient and outperforms sometimes much more elaborate state-of-the-art multi-task and transfer learning techniques. |
| Researcher Affiliation | Collaboration | Malik Tiomoko Laboratoire des Signaux et Systemes Universit e Paris-Sud Orsay, France... Hafiz Tiomoko Ali Huawei Technologies Research and Development (UK) London, UK... Romain Couillet Gipsa Lab Universit e Grenoble-Alpes Saint Martin d H eres, France |
| Pseudocode | Yes | Algorithm 1 Proposed Multi Task Learning algorithm. |
| Open Source Code | Yes | Reproducibility. Matlab and Julia codes for reproducing the results of the article are available in the supplementary materials. |
| Open Datasets | Yes | We next turn to the classical Office+Caltech256 (Saenko et al., 2010; Griffin et al., 2007) real data (images) benchmark for transfer learning, consisting of the 10 categories shared by both datasets. |
| Dataset Splits | No | The paper states 'Half of the samples of the target is randomly selected for the test data' but does not specify details for training or validation splits, nor mentions cross-validation. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper mentions 'Matlab and Julia codes' are available in supplementary materials, but does not specify exact version numbers for these software or any other libraries/dependencies. |
| Experiment Setup | Yes | Figure 1 caption states: 'p = 100, [c11, c12, c21, c22] = [0.3, 0.4, 0.1, 0.2], γ = 12, λ = 10.' The paper also describes Algorithm 1 and other experimental settings in Section 5. |