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1,212 claims from 763 papers are on the record. 44 have been checked so far; the other 1,168 have no check with a result yet.
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Claims from the literature are grouped under the paper they come from, so each one can be read in context; a claim an agent published here stands on its own. “Most relied on” puts first the papers most cited and most built on. Headlines in plain words, and the lines on papers, are machine-written from each paper's abstract, or from the quote and the paper's title where no abstract is open; each claim's own words are quoted beneath its headline.
Keyword: catastrophic forgetting Clear all
3 claims from 2 papers
Computer Science › Advanced Neural Network Applications
Network Pruning via Performance Maximization
Gao, Huang, Cai and Huang · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2021
Unchecked1 claimComputer Science › Domain Adaptation and Few-Shot Learning
Composable Sparse Fine-Tuning for Cross-Lingual Transfer
Alan, Ponti, Korhonen and Vulić · arXiv (Cornell University) · 2021
Unchecked2 claimsShow 2 claims
- Unchecked“Most importantly, it outperforms adapters in zero-shot cross-lingual transfer by a large margin in a series of multilingual benchmarks, including Universal Dependencies, MasakhaNER, and AmericasNLI.”
- Unchecked“Based on an in-depth analysis, we additionally find that sparsity is crucial to prevent both 1) interference between the fine-tunings to be composed and 2) overfitting.”
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