Conditional Neural Processes
Authors: Marta Garnelo, Dan Rosenbaum, Christopher Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo Rezende, S. M. Ali Eslami
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4. Experimental Results |
| Researcher Affiliation | Collaboration | 1Deep Mind, London, UK 2Imperial College London, London, UK. |
| Pseudocode | No | The paper does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We test CNP on two different data sets: the MNIST handwritten digit database (Le Cun et al., 1998) and large-scale Celeb Faces Attributes (Celeb A) dataset (Liu et al., 2015). and Finally, we apply the model to one-shot classification using the Omniglot dataset (Lake et al., 2015) |
| Dataset Splits | No | The paper mentions using 1,200 randomly selected classes as a training set and the remainder as a testing dataset for Omniglot, but it does not specify explicit training/validation/test splits with percentages or counts for any of the datasets used. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not provide specific version numbers for any software dependencies (e.g., deep learning frameworks, libraries). |
| Experiment Setup | Yes | The observed points are encoded using a three layer MLP encoder h with a 128 dimensional output representation ri. The representations are aggregated into a single representation r = 1 n P ri which is concatenated to xt and passed to a decoder g consisting of a five layer MLP. The decoder outputs a Gaussian mean and variance for the target outputs ˆyt. We train the model to maximize the log-likelihood of the target points using the Adam optimizer (Kingma & Ba, 2014). |