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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. ...

...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> ...

...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 ...

...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 ...

...ons in large boxes should be of less significance than small deviations in small boxes. The author claims that predicting the square root of the bounding bo * The loss function treats errors in large bounding boxes the same as small bounding boxes to some extent, which is inconsistent with the relative cont ...

...his similarity measure is large for the points within the same cluster and small for points in different clusters. <math>W</math> has non neagtive elements ...lized cut takes a small value if the clusters <math>C_k</math> are not too small <ref> Ulrike von Luxburg, A Tutorial on Spectral Clustering, Technical Repo ...

...ble if the above representation has just a few large coefficients and many small coefficients. We shall now briefly overview how the transform coding of sig ...<math>\,N</math> may be very large even if the desired <math>\ K</math> is small. ...

...over set <math>\displaystyle A</math> but <math>\displaystyle g</math> is small, then <math>\displaystyle \frac{f}{g} </math> would be large and it would r ...

...ffer from some technical problems. Most importantly, they are limited to a small vocabulary because of complexity and number of parameters that have to be t ...f computing the normalization constant, the authors proposed to use only a small subset <math>v\prime</math> of the target vocabulary at each update<ref> ...

...an adversarial attack where a model is deceived by an attacker by adding a small noise to an input image and as a result, the prediction of the model change ...nce(x,x')=\delta, f(x)\neq f(x')</math>, where <math>\delta</math> is some small number and <math>f(\cdot)</math> is the image label. If the classifier assi ...

...first or last letter of the word. The important thing to note is that even small amounts of noise lead to substantial drops in performance. ...ttle machine learning systems being used so pervasively in the real world. Small changes to the input can lead to dramatic ...

...gested as a candidate is formed by combining basic building blocks to form small modules, then the same basic structures introduced on the building blocks a ...ent_Architecture_Search#Primitive_operations section 2.3] are used to form small networks defined as ''motifs'' by the authors. To combine the outputs of mu ...

...based on the query terms appearing in each document. Stage one produces a small subset of documents where the answer might appear (high recall), and then i ...ize can be billions of documents. In stage one, a retriever would select a small set of potentially relevant documents, which then would be fed to a neural ...