Meta-Learning with Neural Tangent Kernels
Authors: Yufan Zhou, Zhenyi Wang, Jiayi Xian, Changyou Chen, Jinhui Xu
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS We conduct a set of experiments to evaluate the effectiveness of our proposed methods, including a sine wave regression toy experiment, few-shot classification, robustness to adversarial attacks, out-of-distribution generalization and ablation study. ... Table 2: Few-shot classification results on Mini-Image Net and FC-100. |
| Researcher Affiliation | Academia | Yufan Zhou , Zhenyi Wang , Jiayi Xian, Changyou Chen, Jinhui Xu Department of Computer Science and Engineering, State University of New York at Buffalo {yufanzho,zhenyiwa,jxian,changyou,jinhui}@buffalo.edu |
| Pseudocode | Yes | A ALGORITHMS Our proposed algorithms for meta-learning in the RKHS are summarized in Algorithm 1. Algorithm 1 Meta-Learning in RKHS |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or provide links to a code repository for their methods. |
| Open Datasets | Yes | For this experiment, we choose two popular datasets adopted for meta-learning: Mini-Image Net and FC-100 (Oreshkin et al., 2018). ... The CUB (Wah et al., 2011) and VGG Flower Nilsback & Zisserman (2008) are fine-grained datasets used in this experiment... |
| Dataset Splits | Yes | We follow Lee et al. (2020) to split these datasets into meta training/validation/testing sets. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or cloud computing instance specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions 'The Adam optimizer (Kingma & Ba, 2015) is used' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | Similar to Finn et al. (2017), the model architecture is set to be a four-layer convolutional neural network with Re LU activation. The filter number is set to be 32. The Adam optimizer (Kingma & Ba, 2015) is used to minimize the energy functional. Meta batch size is set to be 16 and learning rates are set to be 0.01 for Meta-RKHS-II. |