Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Variational Continual Learning
Authors: Cuong V. Nguyen, Yingzhen Li, Thang D. Bui, Richard E. Turner
ICLR 2018 | Venue PDF | LLM Run Details | Input Tokens: 21,476 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,847 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 EMAIL |
| 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. |