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].
Generalizing across Temporal Domains with Koopman Operators
Authors: Qiuhao Zeng, Wei Wang, Fan Zhou, Gezheng Xu, Ruizhi Pu, Changjian Shui, Christian Gagné, Shichun Yang, Charles X. Ling, Boyu Wang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through empirical evaluations conducted on synthetic and real-world datasets, we validate the effectiveness of our proposed approach. |
| Researcher Affiliation | Academia | 1University of Western Ontario 2Beihang University 3Vector Institute 4Universit e Laval |
| Pseudocode | Yes | Algorithm 1: TKNets (one episode) |
| Open Source Code | No | The paper does not contain an explicit statement about releasing its source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We evaluated our algorithm on 6 datasets, 4 of which were collected in real-world scenarios (RMNIST (Ghifary et al. 2015), Portrait (Kumar, Ma, and Liang 2020; Chen and Chao 2021), Cover Type (Kumar, Ma, and Liang 2020)), and FMo W (Christie et al. 2018)). |
| Dataset Splits | No | The paper describes how data is divided into domains and how support/query sets are used during training and testing (e.g., 'randomly choose the data sets Si, Si+1 from two consecutive domains as support and query sets'), but it does not provide explicit training, validation, and test split percentages or counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'All the baselines and experiments were implemented with Domain Bed package (Gulrajani and Lopez-Paz 2020)', but it does not specify a version number for this package or any other software dependencies. |
| Experiment Setup | No | The paper describes the sampling strategy for support and query sets during training ('sample NB samples from each class k in Si') and mentions that experiments were implemented with 'Domain Bed package (Gulrajani and Lopez-Paz 2020) under the same setting', but it does not explicitly list concrete hyperparameter values such as learning rate, batch size, or number of epochs within the main text. |