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..
Bayesian Meta Sampling for Fast Uncertainty Adaptation
Authors: Zhenyi Wang, Yang Zhao, Ping Yu, Ruiyi Zhang, Changyou Chen
ICLR 2020 | Venue PDF | 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 EMAIL 2 EMAIL |
| 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. |