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

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4 results for “DropBlock”

On the Regularization Properties of Structured Dropoutcs.LG

Dropout improves neural network performance by promoting low-rank solutions.

problem Improving neural network generalization through regularization.

method Analyzing Dropout, DropBlock, and DropConnect as regularizers for linear networks and extending to deep networks.

result Dropout, DropBlock, and DropConnect induce low-rank solutions and can be computed in closed form.

Dropout DropConnect DropBlock Regularization Linear Networks

2019-10-30abs ↗pdf ↗

DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networkscs.LG

The past few years have witnessed the fast development of different regularization methods for deep learning models such as fully-connected deep neural networks (DNNs) and Convolutional Neural Networks (CNNs). Most of previous methods mainly consider to drop features from input data and hidden layers, such as Dropout, …

2018-11-16abs ↗pdf ↗

LocalDrop: A Hybrid Regularization for Deep Neural Networkscs.LG

LocalDrop uses local Rademacher complexity for neural network regularization.

problem Overfitting in deep neural networks.

method Developed a new regularization function based on local Rademacher complexity.

result Demonstrated effectiveness of LocalDrop through extensive experiments.

neural networks regularization overfitting dropout convolutional neural networks

2021-03-01abs ↗pdf ↗

Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learningcs.LG

Paper introduces MCSD, a method for uncertainty estimation in deep learning.

problem Need for reliable uncertainty quantification in deep neural networks.

method Theoretical connection to variational inference and empirical benchmarking of MCSD.

result MCSD achieves competitive predictive accuracy and improves uncertainty ranking.

Monte Carlo Stochastic Depth Uncertainty Estimation Deep Learning Bayesian Approximation Stochastic Depth

2026-04-14abs ↗pdf ↗