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..
Recasting Continual Learning as Sequence Modeling
Authors: Soochan Lee, Jaehyeon Son, Gunhee Kim
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on seven benchmarks, covering both classification and regression, show that sequence models can be an attractive solution for general MCL. |
| Researcher Affiliation | Academia | Soochan Lee Seoul National University EMAIL Jaehyeon Son Seoul National University EMAIL Gunhee Kim Seoul National University EMAIL |
| Pseudocode | Yes | Algorithm 1 Inner loop of conventional SGD-based MCL |
| Open Source Code | Yes | Code is available at https://github.com/soochan-lee/cl-as-seq |
| Open Datasets | Yes | CIFAR-100 [18]. Omniglot [19]. CASIA Chinese Handwriting Database (CASIA; 22). MS-Celeb-1M [10]. |
| Dataset Splits | No | The paper states: 'The tasks are then split into two disjoint sets, one for meta-training and the other for meta-testing.' It does not explicitly mention a separate validation set or split for hyperparameter tuning, distinct from the meta-training and meta-test sets. |
| Hardware Specification | Yes | We compare various aspects of the computational cost using our PyTorch [27] implementation on NVIDIA A40 GPUs which have 48 GB of VRAM. |
| Software Dependencies | No | The paper mentions 'PyTorch [27] implementation' but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | By default, we set K = 20, while additionally testing the K = 100 setting to compare performances with longer episodes. For each task k, the training stream Dtrain k and the test set Dtest k contain five examples each (i.e., five shots). For each experiment, we meta-train for 50K steps with a batch size of 16 (i.e., 16 episodes in parallel) and meta-test with 1,024 episodes. All the models share a similar architecture: 4 layers, 8 heads, and 512 hidden dimensions. |