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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,878 papers · 148 categories

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13 results for “Label-Aware”
Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticitycs.LG

This paper improves neural tangent kernels for better generalization and local elasticity.

problem Performance gap between neural tangent kernels and real-world neural networks.
method Introduces label-aware kernels using Hoeffding decomposition.
result Models trained with proposed kernels simulate NNs better in terms of generalization and local elasticity.
neural tangent kernelsgeneralizationlocal elasticitylabel-awarenessHoeffding decomposition
2020-10-22abs ↗pdf ↗
Label-Aware Graph Convolutional Networkscs.LG

LAGCN improves GCN performance by identifying and using valuable neighbors.

problem Existing GCN models do not identify valuable neighbors, potentially harming performance.
method LAGCN introduces a label-aware edge classifier to refine the graph and enhance learning performance.
result LAGCN significantly improves node classification performance on benchmark datasets.
Graph Convolutional NetworksNode ClassificationLabel-AwareEdge ClassifierValuable Neighbors
2019-07-10abs ↗pdf ↗
Domain Adaptation Under Wireless Network Constraints: When Does It Become Green?stat.ML

Domain Adaptation in 6G wireless networks: When is it green?

problem Energy consumption of Domain Adaptation (UDA) compared to single-task training in 6G wireless networks.
method Investigate energy consumption and propose a method to determine the minimum number of target domains for UDA to be more energy-efficient than retraining.
result Proposed a method to determine the minimum number of target domains for UDA to be more energy-efficient than retraining.
6GDomain AdaptationEnergy ConsumptionLabeling Cost
2026-06-22abs ↗pdf ↗
Orion-Bix: Bi-Axial Attention for Tabular In-Context Learningcs.LG

Orion-Bix combines biaxial attention and meta-learning for tabular few-shot learning.

problem Scaling and generalizing tabular models with mixed numeric and categorical fields, weak feature structure, and limited labeled data.
method Orion-Bix uses biaxial attention and meta-learned in-context reasoning to efficiently capture local and global dependencies.
result Orion-Bix outperforms gradient-boosting baselines and state-of-the-art tabular models on public benchmarks.
tabular learningbiaxial attentionmeta-learningfew-shotmixed data
2025-11-28abs ↗pdf ↗
Rethinking Noisy Label Models: Labeler-Dependent Noise with Adversarial Awarenesscs.LG

Proposes a new model for noisy labels considering multiple labelers and adversarial attacks.

problem Real-world noisy label models with multiple labelers and adversarial attacks.
method Labeler-dependent noise model with adversarial attack vectors.
result State-of-the-art approaches for learning from noisy labels are defeated by adversarial label attacks.
noisy labelslabeler-dependent noiseadversarial attackscrowdsourcingmulti-stage framework
2021-05-28abs ↗pdf ↗
Real-time Adaptation for Condition Monitoring Signal Prediction using Label-aware Neural Processescs.LG

Neural process model improves real-time condition monitoring signal prediction.

problem Real-time adaptation for complex condition monitoring signals.
method Label-aware neural processes encoding and reconstruction.
result Advantages in real-time adaptation, enhanced signal prediction with uncertainty quantification, and joint prediction for labels and signals.
neural processescondition monitoringreal-time adaptationsignal predictionreliability engineering
2024-03-25abs ↗pdf ↗