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COMPANY · ENTITY #1779

Pradeep Shenoy

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2

RESEARCH · 1 SOURCE · Google Research

Google Research: instance-conditional timescales of decay to reweight training data under concept drift

Google Research proposes learning an auxiliary, instance-conditional weighting model that assigns importance scores to training examples as a function of their content and age, combining multiple fixed timescales of decay and meta-learning the assignment alongside the primary model. The method aims to blend benefits of offline and continual learning and yields up to ~15% relative accuracy gains on a large nonstationary photo benchmark (~39M images over 10 years) and improvements across other nonstationary learning benchmarks.

7.0

RESEARCH · 1 SOURCE · Google Research

Google Research proposes 'early readouts' and a 'feature sieve' to mitigate spurious features and simplicity bias

Google Research authors Rishabh Tiwari and Pradeep Shenoy describe two interventions—using predictions from early intermediate layers ('early readouts') and inducing 'feature forgetting' via a 'feature sieve'—to detect and reduce reliance on spurious features and the simplicity bias in deep networks. They apply the early-readout signal to reweight teacher contributions during distillation and use a feature-sieving intervention to encourage models to find more predictive features, reporting improved worst-group accuracy across benchmarks (Waterbirds, CelebA, CivilComments, MNLI) and better generalization to unseen domains compared with prior methods.

7.0