相关实验视频
Updated: May 24, 2025

05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.0K
对分布外样本过度信心放松的轻微倾斜和扩展的分类边际
概括
这项研究引入了一种新的机器学习方法,用于稳健的外分销 (OOD) 检测. 该方法通过准确识别不熟悉的数据而提高安全性,而不会影响性能.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 机器学习模型需要有效的分布外 (OOD) 样本检测才能安全部署.
- 现有的OOD检测方法面临诸如推断速度缓慢,准确度低,对辅助数据的要求以及分类性能下降等局限性.
研究的目的:
- 开发一种用于准确和高效的OOD检测的新方法.
- 为了解决当前后期校准和培训阶段OOD检测技术的局限性.
主要方法:
- 在神经网络训练期间,联合使用具有角边际损失和重量规范性的歧视性表示学习.
- 角边际损失用于扩展分类边际.
- 重量规则化确保在已学习的嵌入空间中有一个轻微倾斜的边缘.
主要成果:
- 拟议的方法构建了一个扩大和轻微倾斜的分类边缘.
- 这有效地减轻了OOD样本的过度自信.
- 实验结果显示,与最先进的OOD探测器相比,性能优越.
结论:
- 拟议的方法通过改善歧视性表示学习,为OOD检测提供了有效的解决方案.
- 它克服了现有方法的缺陷,没有对分类准确性的不利副作用.
- 这一进步有助于安全可靠地部署机器学习模型.
相关概念视频
Margin of Error
3.9K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
3.9K
Confidence Intervals
6.1K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
A...
6.1K
Quantifying and Rejecting Outliers: The Grubbs Test
1.4K
Sometimes, a data set can have a recorded numerical observation that greatly deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier. To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.4K
Interpretation of Confidence Intervals
5.6K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
5.6K
Confidence Coefficient
7.5K
The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
7.5K
Estimating Population Mean with Unknown Standard Deviation
7.6K
In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the...
William S. Gosset (1876–1937) of the...
7.6K

