Chrome Extension
WeChat Mini Program
Use on ChatGLM

A Shrinkage Approach to Improve Direct Bootstrap Resampling under Input Uncertainty

INFORMS Journal on Computing (INFORMS)(2024)CCF BSCI 3区

Georgia Inst Technol | Columbia Univ | Penn State Univ

Cited 0|Views30
Abstract
The goal of this software is to demonstrate the shrinkage bootstrap methods for input uncertainty quantification proposed in "A Shrinkage Approach to Improve Direct Bootstrap Resampling under Input Uncertainty" by Eunhye Song, Henry Lam, and Russell Barton accepted for publication at INFORMS Journal on Computing.
More
Translated text
Key words
bootstrap resampling,input uncertainty,nonparametric,simulation,shrinkage
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本文提出两种新的基于收缩策略的bootstrap方法,以改善在输入不确定性下的直接bootstrap重采样效率,并提高输出置信区间的准确性。

方法】:采用收缩策略降低置信区间内的变异性,以解决传统直接bootstrap方法因输入不确定性导致的过度覆盖问题。

实验】:通过多个数值实验验证所提方法的性能,并研究了每种方法在不同条件下的表现优势;使用的数据集未在文中明确提及,但通过数值实验展示了方法的有效性。