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..
Look-ahead Meta Learning for Continual Learning
Authors: Gunshi Gupta, Karmesh Yadav, Liam Paull
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate La-MAML in settings where the model has to learn a set of sequentially streaming classification tasks. |
| Researcher Affiliation | Academia | Gunshi Gupta Mila, Ude M EMAIL Karmesh Yadav * Carnegie Mellon University EMAIL Liam Paull Mila, Ude M EMAIL |
| Pseudocode | Yes | Algorithm 1 La-MAML : Look-ahead MAML |
| Open Source Code | Yes | The code for our algorithm can be found at: https://github.com/montrealrobotics/La-MAML |
| Open Datasets | Yes | MNIST Rotations, introduced in [17], comprises tasks to classify MNIST digits rotated by a different common angle in [0, 180] degrees in each task. In MNIST Permutations, tasks are generated by shuffling the image pixels by a fixed random permutation... We conduct experiments on the CIFAR-100 dataset... We also evaluate on the Tiny Imagenet-200 dataset... |
| Dataset Splits | Yes | Each task consists of 1000 images for MNIST Permutations and Rotations and 200 images for Many Permutations for training and validation and 2000 images for testing. |
| Hardware Specification | No | No specific hardware details (like GPU or CPU models, or cloud instance types) used for experiments were mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) needed for replication. |
| Experiment Setup | Yes | We use a fully connected network with three hidden layers (256 units per layer) and ReLU non-linearities for the MNIST experiments. For Tiny Imagenet and CIFAR-100, we use a ResNet-18... For the CIFAR-100 and Tiny Imagenet experiments, we use a batch size of 64 and train for 50 epochs. Each method is allowed a replay-buffer, containing upto 200 and 400 samples for CIFAR-100 and Tiny Imagenet respectively. |