New research clarifies implicit assumptions in continual learning models

A new paper published on arXiv explores the implicit assumptions made in continual learning, a field focused on how models accumulate knowledge over time. The research highlights that measurements in continual learning often conflate properties related to the underlying data, the imposed partition of that data into tasks, and the learner itself. By separating these levels, the study demonstrates how varying the granularity of data partitioning, the order of task presentation, and the similarity structure of the data can all lead to different measured outcomes. The paper also reviews existing benchmarks to assess how these properties are reported, noting that when underlying data is undisclosed, its properties are inherited by the model's initial state. AI

IMPACT Clarifies foundational concepts in continual learning, potentially improving model development and evaluation.

RANK_REASON The item is a research paper published on arXiv detailing findings in the field of continual learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• alphaXiv
• arXiv
• arXivLabs
• Bibliographic Explorer
• CatalyzeX Code Finder for Papers
• computer science
• Connected Papers
• continual learning
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• IArxiv Recommender
• Influence Flower
• Litmaps
• machine learning
• ScienceCast
• scite Smart Citations

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →