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...n and the original unknown matrix recovery are provably accurate even when small amount of noise is present and corrupts the few observed entries. The error ...fty}} \leq \sqrt{\mu_B / n_2}</math>, where <math>\mu \geq 1</math> and is small. To see that this assumption guarantees dense vectors, consider the case wh ...

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...nner product between the input feature map and a filter, shifted by <math>\small x</math>. ...nner product between the input feature map and a filter, rotated by <math>\small R</math>. ...

...s and knowledge graphs. Another work trained a label cleaning network by a small set of clean labels and used it to reduce the noise in large-scale noisy la ...instances to the peer network. <math>R(T)</math> governs the percentage of small-loss instances to be used in updating the parameters of each network. ...

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...led data than labelled data. A common situation is to have a comparatively small quantity of labelled data paired with a larger amount of unlabelled data. T ...nd drastically better for when the number of labelled data samples is very small (100 out of 50000). ...

...with normal enumeration if we choose to have a dictionary of the words for small values of <math>\tau</math> ...

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...nected linear layer as the classifier. To make use of order information of small regions it uses hand-crafted n-grams as features in addition to single word ...h convolutional layers. The essence of CNN is to learn word embeddings for small size regions and each kernel of convolutional layer tries to capture a spec ...

...sets if the data is correctly labeled. However, they can be trounced by a small number of incorrect labels, which can be quite challenging to fix. We try t ...alized by [LS], who construct a "hard" training data distribution, where a small percentage of labels is randomly flipped. This label noise then leads to a ...

...of learning tasks, such that it can solve new learning tasks using only a small number of training samples. In this work, we propose a meta-learning algori ...c algorithm for meta-learning that trains a model’s parameters such that a small number of gradient updates will lead to fast learning on a new task. The pa ...

...as shown in Figure 1. After zooming into Figure 1, as shown in Figure 2, a small amount of perturbation led to misclassify a dog as a hummingbird. ...formation. Most of those transformations are sometimes very sensitive to a small change. Think of the DNN as a set of high-dimensional decision boundary. Wh ...

...stablished PDE models exist, but where our amount of available data is too small to guarantee the robustness of convergence in neural network training. In e ...pting to answer the first of the questions above. Specifically, if given a small number of noisy measurements of the solution of the PDE ...

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...tool in statistical learning, which tries to preserve the variability by a small number of principal components. In the classical method, the principal comp ...The diagnostic plot is shown as following. Clearly, ROBPCA distinguishes a small group of bad leverage points which all three other PCA methods fails to rec ...

...especially relevant to situations where the number of observations may be small. ...unctions <math>\,f_t</math> are related to each other, so they all share a small set of features. Formally, the hypothesis is that the functions <math>\,f_t ...

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