Task Weighting in Meta-learning with Trajectory Optimisation
Authors: Cuong C. Nguyen, Thanh-Toan Do, Gustavo Carneiro
TMLR 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | empirically demonstrate that the proposed approach out-performs common hand-engineering weighting methods on two few-shot learning benchmarks. (Abstract) 5 Experiments section begins on page 6, presenting performance metrics, figures, and tables such as "Figures 1a to 1d plot the testing accuracy evaluated on 100 validation tasks" and "Table 1: The classification accuracy averaged on 1,000 random testing tasks". |
| Researcher Affiliation | Academia | Cuong Nguyen EMAIL Australian Institute for Machine Learning The University of Adelaide, Thanh-Toan Do EMAIL Department of Data Science and AI Monash University, Gustavo Carneiro EMAIL Centre for Vision, Speech and Signal Processing University of Surrey. All listed affiliations are academic institutions. |
| Pseudocode | Yes | The complete algorithm of the proposed task-weighting meta-learning approach is outlined in Algorithm 1. (Page 6). Algorithm 1 Task-weighting for meta-learning (Page 6). Algorithm 2 Implementation of i LQR backward to determine the controller of interest. (Appendix D, Page 14). |
| Open Source Code | No | The paper does not contain any explicit statement about releasing code for the methodology described, nor does it provide a direct link to a code repository. Statements like "We release our code..." or links to GitHub are absent. |
| Open Datasets | Yes | The experiments are based on n-way k-shot classification setting used in few-shot learning with tasks formed from Omniglot (Lake et al., 2015) and mini-Image Net (Vinyals et al., 2016) the two most widely-used datasets in meta-learning. |
| Dataset Splits | Yes | For Omniglot, we follow the original train-test split (Lake et al., 2015) by using 30 alphabets for training and 20 alphabets for testing. For mini-Image Net, we follow the standard train-test split that uses 64 classes for training, 16 classes for validation and 20 for testing (Ravi & Larochelle, 2017) in our evaluation. The number of validation (or query) samples is kept at 15 samples per class. |
| Hardware Specification | Yes | Each experiment is carried out on a single NVIDIA Tesla V100 GPU with 32 GB memory following the configuration of NVIDIA DGX-1. The experiments mentioned in Section 5.4 is performed on a single NIVIDIA A100 GPU with 40 GB memory. |
| Software Dependencies | No | Appendix G, "Experimental settings," mentions that "GD is used to obtain ϕ (x)" and the "Adam optimiser" is used. However, it does not specify version numbers for any programming languages (e.g., Python), machine learning frameworks (e.g., PyTorch, TensorFlow), or other relevant software libraries. |
| Experiment Setup | Yes | For all experiments, GD is used to obtain ϕ (x) with 5 iterations and a learning rate of 0.1 for Omniglot and 0.01 for mini-Image Net. The learning rate for the meta-parameters, α, is set at 10 4 for all the setting. The mini-batch size is M = 10 tasks for Omniglot and M = 5 tasks for mini-Image Net. For the Dirichlet concentration of the prior in the exploration and exploitation baselines... we select κ = 1.2. The number of iterations used in i LQR is 2... The number of time steps (or number of mini-batches) is T = 10 for Omniglot and 5 for mini-Image Net. The parameters of the prior on the action ut are µu = 1/M and βu = 10. |