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..
Meta-Curvature
Authors: Eunbyung Park, Junier B. Oliva
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effects of our proposed method on several few-shot learning tasks and datasets. Without any task specific techniques and architectures, the proposed method achieves substantial improvement upon previous MAML variants and outperforms the recent state-of-the-art methods. Furthermore, we observe faster convergence rates of the meta-training process. Finally, we present an analysis that explains better generalization performance with the meta-trained curvature. |
| Researcher Affiliation | Academia | Eunbyung Park Department of Computer Science University of North Carolina at Chapel Hill EMAIL Junier B. Oliva Department of Computer Science University of North Carolina at Chapel Hill EMAIL |
| Pseudocode | Yes | We provide the details of algorithm in appendices. |
| Open Source Code | Yes | The code is available at https://github.com/silverbottlep/meta_curvature |
| Open Datasets | Yes | We evaluated our methods on few-shot regression and few-shot classification tasks over Omniglot [19], mini Imagenet [44], and tiered Imagnet [35] datasets. |
| Dataset Splits | Yes | The mini Imagenet dataset was proposed by [44, 34] and it consists of 100 subclasses out of 1000 classes in the original dataset (64 training classes, 12 validation classes, 24 test classes). |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions using the ADAM optimizer but does not specify its version or the versions of any other software libraries or programming languages used. |
| Experiment Setup | Yes | The network architecture and all hyperparameters are same as [9] and we only introduce the suggested meta-curvature. We follow the experimental protocol in [9] and all hyperparameters and network architecture are same as [9]. We used 4 layers convolutional neural network with the batch normalization followed by a fully connected layer for the final classification. To avoid overfitting, we applied data augmentation techniques suggested in [5, 6]. |