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..
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 | Venue PDF | 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). |