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 [1].
Modular Gaussian Processes for Transfer Learning
Authors: Pablo Moreno-MuƱoz, Antonio Artes, Mauricio Ćlvarez
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive results illustrate the usability of our framework in large-scale and multitask experiments, also compared with the exact inference methods in the literature. |
| Researcher Affiliation | Academia | Pablo Moreno-MuƱoz Antonio ArtĆ©s-RodrĆguez Mauricio A. Ćlvarez Section for Cognitive Systems, Technical University of Denmark (DTU) Dept. of Signal Theory and Communications, Universidad Carlos III de Madrid, Spain Evidence-Based Behavior (e B2), Spain Dept. of Computer Science, University of Shefļ¬eld, UK |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | We provide Pytorch code that allows to easily learn the meta models from GP modules.1 It also includes the baseline methods used. The code is publicly available in the repository: https://github.com/pmorenoz/Modular GP/. |
| Open Datasets | Yes | Figure 2: Modular GPs for {0, 1} MNIST data samples... (iv) Banana dataset... (v) Airline delays (US ļ¬ight data): We took data of US airlines from 2008 (1.5M)... (vi) London household: Based on Hensman et al. (2013), we obtained the register of properties sold in the Greater London County during 2017. |
| Dataset Splits | No | The paper mentions 'test NLPD' for results but does not explicitly provide details about validation splits or how the data was partitioned for training, validation, and testing. |
| Hardware Specification | No | The paper mentions 'providing the computational resources' but does not specify any hardware details like GPU/CPU models, memory, or specific computing environments used for experiments. |
| Software Dependencies | No | The paper states 'We provide Pytorch code' but does not list specific version numbers for PyTorch, Python, or any other software dependencies. |
| Experiment Setup | No | For standard optimization, we used the Adam algorithm (Kingma and Ba, 2015). Details about strategies for initialization and optimization are provided in the appendix. (These details are not in the main text provided). |