arxiv.org/abs/1905.06817v1 http://ringnet.is.tuebingen.mpg.de/ ICML 2019 对抗攻击 | 通过有效的组合优化进行简单的黑盒对抗性攻击 Parsimonious Seungyong Moon, Gaon An, Hyun Oh Song https://arxiv.org/abs/1905.06635v1 https://github.com/snu-mllab/parsimonious-blackbox-attack
parsimonious:Parsimonious 的目标是最快的 arbitrary-lookahead 解析器。 用 Python 实现,基本可用。
《Parsimonious Inference on Convolutional Neural Networks: Learning and applying on-line kernel activation
Thus, we believe the notion of a decision state is a more parsimonious and accurate indication of good
y_{t-i}+\Sigma_{i=1}^q c_i \varepsilon_{t-i}+\varepsilon_t Furthermore, we add five models to have parsimonious
该框架首先寻找最简约(parsimonious)的机制路径,随后利用与已知机制的相似性对候选方案重新排序。
machine learning problem and doubling down on the one that shows the most promise, or selecting the most parsimonious
machine learning problem and doubling down on the one that shows the most promise, or selecting the most parsimonious
Thus, we believe the notion of a decision state is a more parsimonious and accurate indication of good
Thus, we believe the notion of a decision state is a more parsimonious and accurate indication of good
Cloneable { /* * One of these objects is allocated for each in-flight span, so we try to be parsimonious
market agents may be a fundamental property of financial markets and when accounted for can allow for parsimonious
4.2 简洁性(Parsimonious) 奥卡姆剃刀原则在此同样适用。药物化学数据集规模通常有限,高维稀疏的特征空间容易导致维度灾难与过拟合。
4.2 简洁性(Parsimonious) 奥卡姆剃刀原则在此同样适用。药物化学数据集规模通常有限,高维稀疏的特征空间容易导致维度灾难与过拟合。
具有非对称损益重尾特性的金融收益序列低维简约分位数回归 Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential
具有非对称损益重尾特性的金融收益序列低维简约分位数回归 Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential
具有非对称损益重尾特性的金融收益序列低维简约分位数回归 Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential
具有非对称损益重尾特性的金融收益序列低维简约分位数回归 Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential
k-medoids or partitioning around medoids (PAM) 蒸馏|知识提取(1篇) 【1】 Towards extraction of orthogonal and parsimonious constraining the shape of the latent space, it is possible to motivate the orthogonality and extract a set of parsimonious size and the dimensionality of the inputs for a regularized squared loss function, allowing to learn a parsimonious
size and the dimensionality of the inputs for a regularized squared loss function, allowing to learn a parsimonious