REVIEW 2 cited by
Avalanche: an End-to-End Library for Continual Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Learning continually from non-stationary data streams is a long-standing goal and a challenging problem in machine learning. Recently, we have witnessed a renewed and fast-growing interest in continual learning, especially within the deep learning community. However, algorithmic solutions are often difficult to re-implement, evaluate and port across different settings, where even results on standard benchmarks are hard to reproduce. In this work, we propose Avalanche, an open-source end-to-end library for continual learning research based on PyTorch. Avalanche is designed to provide a shared and collaborative codebase for fast prototyping, training, and reproducible evaluation of continual learning algorithms.
Forward citations
Cited by 2 Pith papers
-
HEM: a margin-based loss for visual categorisation tasks
A new margin-based loss, HEM, trains image classifiers that are more robust to unknown and adversarial inputs and better at continual learning and segmentation than cross-entropy-trained models.
-
Addressing Popularity Bias in Third-Party Library Recommendations Using LLMs
Open-source Llama models fail to overcome popularity bias in third-party library recommendations, with low recall across all six tested configurations.
Discussion (0). Continue with ORCID to comment.