Variational Continual Learning
Authors: Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that VCL outperforms state-of-the-art continual learning methods on a variety of tasks, avoiding catastrophic forgetting in a fully automatic way. |
| Researcher Affiliation | Academia | Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner Department of Engineering, University of Cambridge {vcn22,yl494,tdb40,ret26}@cam.ac.uk |
| Pseudocode | Yes | Algorithm 1 Coreset VCL |
| Open Source Code | Yes | An implementation of the methods proposed in this paper can be found at: https://github.com/ nvcuong/variational-continual-learning. |
| Open Datasets | Yes | Permuted MNIST: This is a popular continual learning benchmark (Goodfellow et al., 2014a; Kirkpatrick et al., 2017; Zenke et al., 2017). |
| Dataset Splits | No | The paper mentions training and testing but does not explicitly provide the specific training, validation, and test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not specify software dependencies with version numbers (e.g., specific versions of Python, TensorFlow, PyTorch, or other libraries). |
| Experiment Setup | Yes | For all algorithms, we use fully connected single-head networks with two hidden layers, where each layer contains 100 hidden units with Re LU activations. |