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.
Adapting auxiliary losses using gradient similarity
6 Pith papers cite this work, alongside 95 external citations. Polarity classification is still indexing.
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T3R applies multiple Rotograd matrices and a rotation technique to create surrogate gradients, enabling deeper test-time adaptation in GNNs and yielding 0.172 MAE reduction plus 9.37% relative gains on OGB benchmarks.
A bilevel method learns composite pretraining loss weights online via gradient alignment with a downstream objective, matching tuned baselines at roughly 30% extra cost over one training run.
A Lorentz-model hyperbolic framework for semantic segmentation that integrates with Euclidean networks, provides free uncertainty maps, and is validated on ADE20K, COCO-Stuff, Pascal-VOC and Cityscapes using DeepLabV3, SegFormer, Mask2Former and MaskFormer.
CAML aligns zeroth-order constraints in PINNs and introduces a delay factor to reduce gradient conflicts during training of complex PDE problems.
Auxiliary loss applied to the encoder in learned ICM models produces 27.7% and 20.3% BD-rate improvements for object detection and semantic segmentation versus standard training.
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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T3R: Deeper Test-Time Adaptation for Graph Neural Networks via Gradient Rotation
T3R applies multiple Rotograd matrices and a rotation technique to create surrogate gradients, enabling deeper test-time adaptation in GNNs and yielding 0.172 MAE reduction plus 9.37% relative gains on OGB benchmarks.
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When Losses Align: Gradient-Based Composite Loss Weighting for Efficient Pretraining
A bilevel method learns composite pretraining loss weights online via gradient alignment with a downstream objective, matching tuned baselines at roughly 30% extra cost over one training run.
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Lorentz Framework for Semantic Segmentation
A Lorentz-model hyperbolic framework for semantic segmentation that integrates with Euclidean networks, provides free uncertainty maps, and is validated on ADE20K, COCO-Stuff, Pascal-VOC and Cityscapes using DeepLabV3, SegFormer, Mask2Former and MaskFormer.
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Mitigating Gradient Pathology in PINNs through Aligned Constraint
CAML aligns zeroth-order constraints in PINNs and introduces a delay factor to reduce gradient conflicts during training of complex PDE problems.
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Improving Image Coding for Machines through Optimizing Encoder via Auxiliary Loss
Auxiliary loss applied to the encoder in learned ICM models produces 27.7% and 20.3% BD-rate improvements for object detection and semantic segmentation versus standard training.