Variational Continual Bayesian Meta-Learning
Authors: Qiang Zhang, Jinyuan Fang, Zaiqiao Meng, Shangsong Liang, Emine Yilmaz
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on tasks from non-stationary distributions show that VC-BML is superior in transferring knowledge among diverse tasks and alleviating catastrophic forgetting in an online setting. Finally, extensive experiments show our VC-BML algorithm outperforms seven state-of-the-art baselines on non-stationary task distributions from four benchmark datasets. |
| Researcher Affiliation | Collaboration | Qiang Zhang1,2,3 , Jinyuan Fang4 , Zaiqiao Meng5,6, Shangsong Liang4,6 , Emine Yilmaz7 1 Hangzhou Innovation Center, Zhejiang University, China 2 College of Computer Science and Technology, Zhejiang University, China 3 AZFT Knowledge Engine Lab, China; 4 Sun Yat-sen University, China 5 University of Glasgow, United Kingdom 6 Mohamed bin Zayed University of Artiļ¬cial Intelligence, United Arab Emirates 7 University College London, United Kingdom |
| Pseudocode | Yes | Pseudo codes for VC-BML are in Section D of Appendix. |
| Open Source Code | Yes | Codes and dataset to reproduce the experiments are included in the supplemental material. |
| Open Datasets | Yes | Similar to previous works [4, 25], we conduct experiments on four datasets: Omniglot [35], CIFARFS [36], mini Imagenet [37] and VGG-Flowers [38]. |
| Dataset Splits | Yes | Similar to the conventional meta-learning setting [14], the dataset Dt is split into a support set DS t = {(xi, yi)}NS t i=1 for training and a query set DQ t = {(xi, yi)}NQ t i=1 for validation. We tune the hyperparameters based on the validation sets. |
| Hardware Specification | Yes | The amount and the type of computing resource used in our experiments are described in Appendix. We acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with their version numbers in the main text or the provided sections. While experimental details are mentioned to be in the Appendix, the main paper doesn't specify versioned software. |
| Experiment Setup | Yes | We tune the hyperparameters based on the validation sets. More details can be found in Section E of Appendix. |