Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Modular Meta-Learning with Shrinkage
Authors: Yutian Chen, Abram L. Friesen, Feryal Behbahani, Arnaud Doucet, David Budden, Matthew Hoffman, Nando de Freitas
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we demonstrate that our method discovers a small set of meaningful task-specific modules and outperforms existing metalearning approaches in domains like few-shot text-to-speech that have little task data and long adaptation horizons. |
| Researcher Affiliation | Industry | Deep Mind London, UK EMAIL |
| Pseudocode | Yes | Figure 1: (Left) Structure of a typical meta-learning algorithm. (Right) Bayesian shrinkage graphical model. The shared meta parameters φ serve as the initialization of the neural network parameters for each task θt. The σ are shrinkage parameters. By learning these, the model automatically decides which subsets of parameters (i.e., modules) to fix for all tasks and which to adapt at test time. |
| Open Source Code | No | The paper does not explicitly provide an unambiguous statement or link to its own open-source code for the methodology described. |
| Open Datasets | Yes | We use the augmented Omniglot protocol of Flennerhag et al. [4], which necessitates long-horizon adaptation. |
| Dataset Splits | Yes | We use 30 training alphabets (T = 30), 15 training images (K = 15), and 5 validation images per class. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU types, or cloud instance specifications). |
| Software Dependencies | No | The paper mentions using specific algorithms like 'conjugate gradient algorithm' and 'Adam [46]', but does not provide specific software names with version numbers (e.g., 'PyTorch 1.9', 'TensorFlow 2.0'). |
| Experiment Setup | Yes | Following Flennerhag et al. [4], we use a 4-layer convnet and perform 100 steps of task adaptation. |