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 [1].
Meta-Learning Universal Priors Using Non-Injective Change of Variables
Authors: Yilang Zhang, Alireza Sadeghi, Georgios Giannakis
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments conducted on three few-shot learning datasets validate the superiority of data-driven priors over the prespecified ones, showcasing its pronounced effectiveness when dealing with extremely limited data resources. In this section, we test and showcase the empirical superiority of Meta NCo V on both synthetic and real datasets. |
| Researcher Affiliation | Academia | Yilang Zhang Department of ECE University of Minnesota Minneapolis, MN 55414 EMAIL Alireza Sadeghi Department of ECE University of Minnesota Minneapolis, MN 55414 EMAIL Georgios B. Giannakis Department of ECE University of Minnesota Minneapolis, MN 55414 EMAIL |
| Pseudocode | Yes | Algorithm 1 Meta NCo V algorithm |
| Open Source Code | Yes | Codes for reproducing the results are available at https://github.com/zhangyilang/Meta NCo V. |
| Open Datasets | Yes | Mini Image Net [55] contains 60, 000 images sampled from the full Image Net (ILSVRC-12) dataset... Tiered Image Net [42] is a larger subset of the Image Net dataset... CUB-200-2011 [57] is an extended version of the Caltech-UCSD Birds(CUB)-200 dataset... |
| Dataset Splits | Yes | The dataset is divided into a training subset Dtrn t Dt, and a validation subset Dval t := Dt \ Dtrn t. In the experiments, we adopt the dataset split suggested by [41], where 64, 16 and 20 disjoint classes can be accessed during the training, validation, and testing phases of meta-learning. |
| Hardware Specification | Yes | Our codes are run on a server equipped with an Intel Core i7-12700 CPU, and an NVIDIA RTX A5000 GPU. |
| Software Dependencies | No | The paper mentions optimizers like SGD with Nesterov momentum and Adam but does not list specific software libraries or their version numbers (e.g., PyTorch, Python, CUDA versions) used for implementation. |
| Experiment Setup | Yes | The hyperparameters used for the few-shot classification experiments are the same as those in MAML [10], which are listed in Table 6. To enhance the statbility of the training process, we use SGD with Nesterov momentum instead of Adam as the optimizer for (10a). |