Task-Robust Model-Agnostic Meta-Learning
Authors: Liam Collins, Aryan Mokhtari, Sanjay Shakkottai
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also provide an upper bound on the new task generalization error that captures the advantage of minimizing the worst-case task loss, and demonstrate this advantage in sinusoid regression and image classification experiments. |
| Researcher Affiliation | Academia | Liam Collins ECE Department University of Texas at Austin Austin, TX 78712 EMAIL Aryan Mokhtari ECE Department University of Texas at Austin Austin, TX 78712 EMAIL Sanjay Shakkottai ECE Department University of Texas at Austin Austin, TX 78712 EMAIL |
| Pseudocode | Yes | Algorithm 1 Task-Robust MAML (TR-MAML) |
| Open Source Code | Yes | 1The code for TR-MAML is available at: https://github.com/lgcollins/tr-maml. |
| Open Datasets | Yes | We experiment in this setting using the Omniglot [19] and mini-Image Net [37] datasets. |
| Dataset Splits | Yes | We use the same (meta-) train/validation/test splits as in [36]. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Both algorithms use one SGD step as the inner learning algorithm, and the same fully-connected network architecture as in [12] for the learning model. ... We meta-train for 60,000 iterations with a batch size of 2 task instances, and 5 steps of gradient descent for local adaptation. |