On-device research index

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.

168,694 papers · 148 categories

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3 results for “re-labeling”

ActiveLab: Active Learning with Re-Labeling by Multiple Annotatorscs.LG

ActiveLab improves classifier accuracy with fewer annotations by re-labeling.

problem Imperfect labels from multiple annotators in real-world data.

method ActiveLab automatically decides when to re-label examples for better classifier training.

result ActiveLab trains more accurate classifiers with fewer annotations.

active learning re-labeling multiple annotators classifier accuracy fewer annotations

2023-01-27abs ↗pdf ↗

FedReLa: Imbalanced Federated Learning via Re-Labelingstat.ML

FedReLa: A novel data-level approach for imbalanced federated learning

problem Improving accuracy of federated learning models under class imbalance and data heterogeneity

method Re-labeling samples with a feature-dependent label re-allocator

result Significant improvements in accuracy for minority classes and overall accuracy on stepwise-imbalanced and long-tailed datasets

federated learning class imbalance data heterogeneity accuracy improvement

2026-06-24abs ↗pdf ↗

Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuationscs.CL

CNN predicts stock fluctuations using company news headlines.

problem Predicting next-day stock fluctuations based on company-specific news.

method Convolutional Neural Network (CNN) with reduced filter dimensions and multiple hidden layers. Fine-tuned word embeddings and various filter widths.

result 61.7% classification accuracy achieved using pre-learned embeddings.

stock prediction CNN company news convolutional filters word embeddings

2020-06-22abs ↗pdf ↗