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Paper 2011.10568

by Azhar Shaikh ID: arxiv-paper--2011.10568

Task-incremental learning involves the challenging problem of learning new tasks continually, without forgetting past knowledge. Many approaches address the problem by expanding the structure of a shared neural network as tasks arrive, but struggle to grow optimally, without losing past knowledge. W...

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Registry ID arxiv-paper--2011.10568
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BibTeX
@misc{arxiv_paper__2011.10568,
  author = {Azhar Shaikh},
  title = {Paper 2011.10568 Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2011.10568v1}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
Azhar Shaikh. (2026). Paper 2011.10568 [Paper]. Free2AITools. https://arxiv.org/abs/2011.10568v1

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Top 19% Overall Impact
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The Nexus Index for Paper 2011.10568 aggregates Popularity (P:0), Velocity (V:0), and Credibility (C:0). The Utility score (U:0) represents deployment readiness, context efficiency, and structural reliability within the Nexus ecosystem.

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πŸ“ Executive Summary

"Task-incremental learning involves the challenging problem of learning new tasks continually, without forgetting past knowledge. Many approaches address the problem by expanding the structure of a shared neural network as tasks arrive, but struggle to grow optimally, without losing past knowledge. We present a new framework, Learn to Bind and Grow, which learns a neural architecture for a new task incrementally, either by binding with layers of a similar task or by expanding layers which are ..."

❝ Cite Node

@article{Shaikh2020ArXiv,
  title={ArXiv 2011.10568 Technical Profile},
  author={Azhar Shaikh and Nishant Sinha},
  journal={arXiv preprint arXiv:arxiv-paper--2011.10568},
  year={2020}
}

πŸ‘₯ Collaborating Minds

Azhar Shaikh Nishant Sinha

Abstract & Analysis

Task-incremental learning involves the challenging problem of learning new tasks continually, without forgetting past knowledge. Many approaches address the problem by expanding the structure of a shared neural network as tasks arrive, but struggle to grow optimally, without losing past knowledge. We present a new framework, Learn to Bind and Grow, which learns a neural architecture for a new task incrementally, either by binding with layers of a similar task or by expanding layers which are more likely to conflict between tasks. Central to our approach is a novel, interpretable, parameterization of the shared, multi-task architecture space, which then enables computing globally optimal architectures using Bayesian optimization. Experiments on continual learning benchmarks show that our framework performs comparably with earlier expansion based approaches and is able to flexibly compute multiple optimal solutions with performance-size trade-offs.

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πŸ†” Identity & Source

id
arxiv-paper--2011.10568
author
Azhar Shaikh
tags
arxiv:cs.LGarxiv:cs.AIarxiv:cs.NEneural

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