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.

168,742 papers · 148 categories

Trend · papers per month

count % of corpus

5 results for “data-abundant”

From Linear to Nonlinear: Provable Weak-to-Strong Generalization through Feature Learningcs.LG

Paper analyzes weak-to-strong generalization in CNNs, identifying data-scarce and data-abundant regimes.

problem Weak-to-strong generalization in CNNs trained on weak models.

method Formal analysis of gradient descent dynamics in data-scarce and data-abundant regimes.

result Identifies two regimes and distinct mechanisms of generalization in each.

CNNs gradient descent weak-to-strong generalization data-scarce data-abundant

2025-10-28abs ↗pdf ↗

Estimating the Value of Evidence-Based Decision Makingstat.ME

This article introduces a framework to estimate the value of evidence-based decision making.

problem Lack of empirical tools to assess the value of evidence-based decision making and optimize statistical precision.

method Empirical framework using parametric and nonparametric empirical Bayes methods.

result The value of statistical evidence depends on how organizations translate it into policy decisions.

Evidence-Based Decision Making Statistical Precision Empirical Bayes Decision Rules Value of Evidence

2023-06-21abs ↗pdf ↗

Upsample or Upweight? Balanced Training on Heavily Imbalanced Datasetscs.CL

Investigates upsampling vs. upweighting for balanced training on skewed datasets.

problem Balancing training on heavily imbalanced datasets with scarce data.

method Theoretical and empirical analysis of upsampling and upweighting strategies.

result Upsampling and upweighting diverge under stochastic gradient descent, with upsampling leading to faster convergence but higher overfitting risk.

Data Imbalance Multilingual Setting Gradient Variance Overfitting Data Re-weighting

2024-10-06abs ↗pdf ↗

Improving the Accuracy of Global Forecasting Models using Time Series Data Augmentationcs.LG

Study improves GFM accuracy with time series augmentation.

problem Limited time series data hinders GFM performance.

method Data augmentation techniques (GRATIS, MBB, DBA) and transfer learning.

result Significant improvement in GFM accuracy over baseline.

time series forecasting data augmentation recurrent neural networks forecasting competitions transfer learning

2020-08-06abs ↗pdf ↗

The Effectiveness of Discretization in Forecasting: An Empirical Study on Neural Time Series Modelscs.LG

This study examines how discretization improves neural forecasting models.

problem Improving predictive performance of neural forecasting models.

method Empirical investigation of data binning techniques on various neural forecasting architectures.

result Data binning almost always improves forecasting accuracy, but the type of binning is less important.

neural forecasting data binning time series deep learning predictive performance

2020-05-20abs ↗pdf ↗