Towards Sample-efficient Overparameterized Meta-learning
Authors: Yue Sun, Adhyyan Narang, Ibrahim Gulluk, Samet Oymak, Maryam Fazel
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments on real and synthetic data verify our insights on overparameterized meta-learning. |
| Researcher Affiliation | Academia | Yue Sun University of Washington yuesun@uw.edu Adhyyan Narang University of Washington adhyyan@uw.edu Halil Ibrahim Gulluk Bogazici University hibrahimgulluk@gmail.com Samet Oymak University of California, Riverside oymak@ece.ucr.edu Maryam Fazel University of Washington mfazel@uw.edu |
| Pseudocode | Yes | Algorithm 1 Constructing the optimal representation |
| Open Source Code | Yes | The code for this paper is in https://github.com/sunyue93/Rep-Learning. |
| Open Datasets | Yes | A Res Net-50 network pretrained on Imagenet was utilized to obtain a representation of R features for classification on CIFAR-10. |
| Dataset Splits | Yes | Data for Representation Learning (Phase 1). We have T tasks, each with n1 training examples... Data for Few-Shot Learning (Phase 2). Few-shot dataset has n2 examples (yi, xi)n2 j=1 i.i.d. Dβ. In Figure 1(a), 'Few shot train size = 20 Few shot train size = 100 Few shot train size = 500' are explicitly given. |
| Hardware Specification | No | The paper states '[N/A]' for question 3(d) regarding the total amount of compute and type of resources used for experiments, indicating no hardware specifications are provided. |
| Software Dependencies | No | The paper mentions software components like 'Res Net-50 network' but does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | A Res Net-50 network pretrained on Imagenet was utilized to obtain a representation of R features for classification on CIFAR-10. All layers except the final (softmax) layer are frozen and are treated as a fixed feature-map. We then train the final layer of the network for the downstream task which yields a linear classifier on pretrained features. In Fig. 5, we plot the error of the whole meta-learning algorithm. d = 100, n2 = 40, T = 200, ΣT = (I20, 0.05 I80), ΣF = I100. |