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..
Bootstrapping neural processes
Authors: Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the efficacy of BNP on various types of data and its robustness in the presence of model-data mismatch. In this section, we compare the baseline NP classes (CNP, NP, CANP, and ANP) to our models (BNP, BANP) on both synthetic and real-world datasets. |
| Researcher Affiliation | Collaboration | KAIST1, Daejeon, South Korea, AITRICS2, Seoul, South Korea, POSTECH3, Pohang, South Korea, University of Oxford4, Oxford, England |
| Pseudocode | No | The paper describes the methodology in prose, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of the described methodology. |
| Open Datasets | Yes | We trained the models for EMNIST using the first 10 classes and tested on the remaining 37 classes. [3] for EMNIST and [15] for Celeb A |
| Dataset Splits | Yes | We train with 4000 tasks and validate with 200 tasks. During testing, we evaluate 100 tasks. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library names like PyTorch 1.x or Python 3.x). |
| Experiment Setup | Yes | We fixed k = 4 for all of our experiments. We train for 1000 epochs with Adam optimizer, using learning rate 10e-3. We use early stopping with patience 200. |