keyword : overfitting, benign, adversarial attacks, deep learning 0.Abstract 文章主要学习引起深度学习对抗脆弱性问题的两个原因:1. bad data 2. pooly trained models. 当利用SGD 训练深度神经网络的时候可以在存在标签噪音的情况下训练中达到zero error并在测试数据中展现很好的
There is an increasing realization that algorithmic inductive biases are central in preventing overfitting; empirically, we often see a benign overfitting phenomenon in overparameterized settings for natural learning algorithms, such as stochastic gradient descent (SGD), where little to no explicit ...
There is an increasing realization that algorithmic inductive biases are central in preventing overfitting; empirically, we often see a benign overfitting phenomenon in overparameterized settings for natural learning algorithms, such as stochastic gradient descent (SGD), where little to ...
We study benign overfitting in the setting of nonparametric regression under mean squared risk, and on the scale of Hölder classes. We construct a local polynomial estimator of the regression function that is minimax optimal on a Hölder class with any given smoothness, and that is a continuo...
implicit bias 在之前的简读中提到过,指的是对神经网络的优化过程会自然偏向于寻找具有某种良好性质的解,而无需增加 regularization term,benign overfitting 姑且翻译为良性过拟合,指的是对带有noise的数据集进行训练并且将 training error收敛到0之后会发现训练得到的模型在测试集上也有很好的表现。
We call this intermediate regime tempered overfitting, and we initiate its systematic study. We first explore this phenomenon in the context of kernel (ridge) regression (KR) by obtaining conditions on the ridge parameter and kernel eigenspectrum under which KR exhibits each of the three behaviors....
In-distribution research on high-dimensional linear regression has led to the identification of a phenomenon known as extit{benign overfitting}, in which linear interpolators overfit to noisy training labels and yet still generalize well. ... N Mallinar,A Zane,S Frei,... 被引量: 0发表: 202...
This is an important factor in all prediction models, but may be particularly the case with the methods used in this study. Because recursive partitioning findings are derived from the study population, they are particularly prone to overfitting. This is a major limitation of this component of ...
The effectiveness of the technique is to require few computational resources and does not need any tuning.The major strength of AdaBoost technique is its insusceptibility to overfitting problem. The Extremely Randomized Trees (ET) model is relative to data with small number of samples and works ...
In the presence of noise, the standard maximum margin algorithm described above can be subject to overfitting, and more sophisticated techniques should be used. This problem arises because the maximum margin algorithm always finds a perfectly consistent hypothesis and does not tolerate training error. ...