CAML meta-learns a progressively refined inductive bias from active-learning queries to improve robustness to spurious correlations, reporting accuracy gains on minority groups across several benchmarks.
Meta-Learning in Neural Networks: A Survey , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4representative citing papers
KUP-BI distills continuation-style knowledge from a train-only historical library to supply an approximate post-target proxy that is fused into forecasting backbones for improved performance on public datasets.
GeMCL scales few-shot spoken word classification to 1000 classes with 5 shots each, matching frozen-HuBERT baseline performance while adapting 2000 times faster on less than half the data.
Multilingual generative meta-learning for spoken word classification shows small gains over monolingual models, with unique data volume mattering more than the number of languages.
citing papers explorer
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Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations
CAML meta-learns a progressively refined inductive bias from active-learning queries to improve robustness to spurious correlations, reporting accuracy gains on minority groups across several benchmarks.
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Beyond Extrapolation: Knowledge Utilization Paradigm with Bidirectional Inspiration for Time Series Forecasting
KUP-BI distills continuation-style knowledge from a train-only historical library to supply an approximate post-target proxy that is fused into forecasting backbones for improved performance on public datasets.
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Scaling few-shot spoken word classification with generative meta-continual learning
GeMCL scales few-shot spoken word classification to 1000 classes with 5 shots each, matching frozen-HuBERT baseline performance while adapting 2000 times faster on less than half the data.
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Does language matter for spoken word classification? A multilingual generative meta-learning approach
Multilingual generative meta-learning for spoken word classification shows small gains over monolingual models, with unique data volume mattering more than the number of languages.