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..
Gradient based sample selection for online continual learning
Authors: Rahaf Aljundi, Min Lin, Baptiste Goujaud, Yoshua Bengio
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments This section serves to validate our approach and show its effectiveness at dealing with continual learning problems where task boundaries are not available. |
| Researcher Affiliation | Academia | Rahaf Aljundi KU Leuven EMAIL Min Lin Mila EMAIL Baptiste Goujaud Mila EMAIL Yoshua Bengio Mila EMAIL |
| Pseudocode | Yes | Algorithm 1 IQP Sample Selection; Algorithm 2 Greedy Sample Selection |
| Open Source Code | Yes | The code is available at https://github.com/rahafaljundi/Gradient-based-Sample-Selection |
| Open Datasets | Yes | Disjoint MNIST: MNIST dataset divided into 5 tasks based on the labels with two labels in each task. Permuted MNIST: We perform 10 unique permutations on the pixels of the MNIST images. Disjoint CIFAR-10: Similar to disjoint MNIST, the dataset is split into 5 tasks according to the labels, with two labels in each task. |
| Dataset Splits | No | The paper states training and testing details, but does not explicitly provide information about a separate validation set split (e.g., percentages, counts, or explicit mention of a validation set). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using SGD optimizer and a neural network framework implicitly (PyTorch/TensorFlow are common in such papers, but not specified), but does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all experiments, we use a fixed batch size of 10 samples and perform few iterations over a batch (1-5)... Following [10], for disjoint and permuted MNIST we use a two-layer neural network with 100 neurons each while for CIFAR-10 we use Res Net18... In all experiments, we use SGD optimizer with a learning rate of 0.05 for disjoint MNIST and permuted MNIST and 0.01 for disjoint Cifar-10. |