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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Generalized Variational Continual Learning
Authors: Noel Loo, Siddharth Swaroop, Richard E Turner
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, in Section 5 we test GVCL and GVCL with Fi LM layers on many standard benchmarks... |
| Researcher Affiliation | Academia | Noel Loo, Siddharth Swaroop & Richard E. Turner University of Cambridge EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/yolky/gvcl |
| Open Datasets | Yes | It is derived from the HASYv2 dataset (Thoma, 2017)... For our Split-MNIST experiment, in addition to the standard 5 binary classification tasks for Split MNIST, we add 5 more binary classification tasks by taking characters from the KMNIST dataset (Clanuwat et al., 2018)... The popular Split-CIFAR dataset, introduced in Zenke et al. (2017)... |
| Dataset Splits | Yes | Early stopping based on the validation set was used. 10% of the training set was used as validation for these methods, and for Easy and Hard CHASY, 8 samples per class form the validation set (which are disjoint from the training samples or test samples). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using a Github repository for HAT but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Baseline MAP algorithms were trained with SGD with a decaying learning starting at 5e-2 with a maximum epochs of 200 per task... For VI models, we used Adam optimizer with a learning rate of 1e-4 for Split-MNIST and Mixture, and 1e-3 for Easy-CHASY, Hard-CHASY and Split-CIFAR... All experiments (both the baselines and VI methods) use a batch size of 64... Table 3: Best (selected) hyperparameters for continual learning experiments for various algorithms. |