A Principled Approach for Learning Task Similarity in Multitask Learning
Authors: Changjian Shui, Mahdieh Abbasi, Louis-Émile Robitaille, Boyu Wang, Christian Gagné
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We assess our new algorithm empirically on several benchmarks, showing not only that we find interesting and robust task relations, but that the proposed approach outperforms the baselines, reaffirming the benefits of theoretical insight in algorithm design. |
| Researcher Affiliation | Academia | 1Universit e Laval 2University of Pennsylvania {changjian.shui.1, mahdieh.abbasi.1, louis-emile.robitaille.1}@ulaval.ca, boyuwang@seas.upenn.edu, christian.gagne@gel.ulaval.ca |
| Pseudocode | Yes | Algorithm 1 AMTNN updating algorithm |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We first evaluate our algorithm on three benchmark datasets of digit recognition, which are datasets, MNIST, MNIST M, and SVHN. We also evaluate the proposed algorithm on Amazon reviews datasets. We extract reviews from four product categories: Books, DVD, Electronics and Kitchen appliances. |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly describe a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | No | The paper describes the general network architecture (e.g., 'Le Net-5 architecture', 'two convolutional layers', 'fully connected layers') and some training techniques ('gradient reversal', 'gradient penalty') but does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed system-level training configurations. |