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..
Structured Prediction for Conditional Meta-Learning
Authors: Ruohan Wang, Yiannis Demiris, Carlo Ciliberto
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we show that TASML improves the performance of existing meta-learning models, and outperforms the state-of-the-art on benchmark datasets. We empirically evaluate TASML on several competitive few-shot classification benchmarks, including datasets derived from IMAGENET and CIFAR respectively. |
| Researcher Affiliation | Academia | Ruohan Wang, Yiannis Demiris, Carlo Ciliberto Dept. of Electrical and Electronic Engineering Imperial College London London, UK EMAIL |
| Pseudocode | Yes | Algorithm 1 TASML |
| Open Source Code | Yes | TASML implementation is available at https://github.com/RuohanW/Tasml |
| Open Datasets | Yes | We empirically evaluate TASML on several competitive few-shot classification benchmarks, including datasets derived from IMAGENET and CIFAR respectively. We perform experiments1 on C-way-K-shot learning within the episodic formulation of [53]. ... We evaluate the proposed method against a wide range of meta-learning algorithms on three few-shot learning benchmarks: the mini IMAGENET, tiered IMAGENET and CIFAR-FS datasets. |
| Dataset Splits | Yes | For training, validation and testing, we sample three separate meta-datasets Str, Sval and Sts, each accessing a disjoint set of classes (e.g. no class in Sts appears in Str or Sval). Dval contains samples from the same C classes for estimating model generalization and training meta-learner. |
| Hardware Specification | Yes | Tab. 4, which reports the average number of meta-gradient steps per second on a single Nvidia GTX 2080. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies (e.g., libraries, frameworks like PyTorch). |
| Experiment Setup | Yes | We consider the commonly used 5-way-1-shot and 5-way-5-shot settings. In our experiments we chose M to be 1% of N. Appendix B reports further experimental details including network specification and hyperparameter choice. |