Papers with differentiable neural architecture search

2 papers
LECO: Improving Early Exiting via Learned Exits and Comparison-based Exiting Mechanism (2023.acl-srw)

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Challenge: Recent work on dynamic early exiting has neglected the intermediate exits’ architectural designs.
Approach: They propose a framework for learning exits and COmparison-based early exiting to improve PTMs’ early exit performance.
Outcome: The proposed framework achieves the SOTA performance on multi-exit BERT training and dynamic early exiting on pre-trained models.
Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition (D19-1)

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Challenge: Neural architecture search (NAS) is a popular approach for finding new models and freeing researchers from the hard work of designing network architectures.
Approach: They propose differentiable neural architecture search methods for natural language processing . they remove the softmax-local constraint and apply it to named entity recognition .
Outcome: The proposed method outperforms strong baselines on the language modeling task.

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