Bayesian Meta Sampling for Fast Uncertainty Adaptation
Authors: Zhenyi Wang, Yang Zhao, Ping Yu, Ruiyi Zhang, Changyou Chen
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate the efficiency and effectiveness of the proposed framework, obtaining better sample quality and faster uncertainty adaption compared to related methods. |
| Researcher Affiliation | Academia | Zhenyi Wang 1, Yang Zhao 1, Ping Yu 1, Ruiyi Zhang 2, Changyou Chen 1 1 State University of New York at Buffalo 2 Duke University 1 {zhenyiwa, yzhao63, pingyu, changyou}@buffalo.edu 2 ryzhang@cs.duke.edu |
| Pseudocode | Yes | Algorithm 1 Meta training, MAML with ELBO. Algorithm 2 Meta testing, MAML with ELBO. |
| Open Source Code | Yes | Our code is made available at: https://github.com/zheshiyige/meta-sampling.git. |
| Open Datasets | Yes | MNIST and CIFAR-10 (Krizhevsky, 2009). and UCI repository: Australian (15 features, 690 samples), German (25 features, 1000 samples), Heart (14 features, 270 samples). |
| Dataset Splits | Yes | For each task, the dataset Dτ is divided into two sets Dtr τ {Xtr τ , ytr τ } and Dval τ {Xval τ , yval τ }. and Mini-Imagenet dataset, consisting of 64, 16, and 20 classes for training, validation and testing, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models or types of computing resources used for the experiments. |
| Software Dependencies | No | The paper mentions software like Adam, RBF kernel, and Pytorch MAML implementation, but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | The model is trained with Adam (Kingma & Ba, 2015) with a learning rate of 0.005. We use 1000 particles to approximate the distribution. We set the weight λ in the WGF to be 1e-4. The meta sampler is trained for 1000 iterations. and We set the batch size to be 128, and the learning rate to be 0.002. |