Neural Variational Dropout Processes
Authors: Insu Jeon, Youngjin Park, Gunhee Kim
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | 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. |