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..
Neural Variational Dropout Processes
Authors: Insu Jeon, Youngjin Park, Gunhee Kim
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compared the proposed method with other meta-learning approaches in the few-shot learning tasks such as 1D stochastic regression, image inpainting, and classification. The results show the excellent performance of NVDPs. |
| Researcher Affiliation | Collaboration | Insu Jeon1, Youngjin Park2, Gunhee Kim1 1Seoul National University; 2Everdoubling LLC., Seoul, South Korea |
| Pseudocode | No | The paper does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a direct statement or link to its own open-source code for the described methodology. |
| Open Datasets | Yes | The paper explicitly states the use of well-known datasets: 'GP Dataset', 'MNIST (Le Cun et al., 1998)', 'Celeb A (Liu et al., 2015)', 'Omniglot (Lake et al., 2015)', and 'Mini Imagenet (Ravi & Larochelle, 2017)'. |
| Dataset Splits | Yes | And the task data points are split into a disjoint sample of m contexts and n targets as m U(3, 97) and n U[m + 1, 100), respectively. In the test or validation, the numbers of contexts and targets were chosen as m U(3, 97) and n = 400 m, respectively. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU/GPU models or memory used for the experiments. |
| Software Dependencies | No | The paper mentions 'Adam optimizer (Kingma & Ba, 2015)' but does not provide specific version numbers for Adam or any other software dependencies. |
| Experiment Setup | Yes | The agent NN with 4 hidden layers of 128 units with Le LU activation... All models were trained with Adam optimizer (Kingma & Ba, 2015) with learning rate 5e-4 and 16 task-batches for 0.5 million iterations. ... Adam optimizer with a learning rate 4e-4 and 16 task batches with 300 epochs were used for training. |