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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |