Bootstrapping neural processes
Authors: Juho Lee, Yoonho Lee, Jungtaek Kim, Eunho Yang, Sung Ju Hwang, Yee Whye Teh
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | 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. |