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...out. However, the shortcomings of the existing methods are inevitable due to data sparsity and scalability. With deep learning and Convolutional Neural ...interpret. Therefore, the concept of compressing label space is introduced to effectively create lower-dimensional label vectors using either linear or n ...

...order to get a distribution for the probability 'p' of a Binomial, we have to divide the Binomial distribution by n. This new distribution has the same s # Compute <math>\displaystyle \delta = p_1 - p_2</math> in order to get n values for <math>\displaystyle \delta</math>; ...

...cases, we want to reduce the number of dimensions because we always want to save computations. The reason behind this kind of pooling method is based o ...od is that, it only passes the local patterns into the next layer. That is to say, if our original data set doesn't have the good property of neighborhoo ...

The paper presents an unsupervised method to machine translation using only monoligual corpora without any alignment bet The general approach of the methodology is to first use a unsupervised word-by-word translation model proposed by [Connea ...

...lling a fair die repetitively to produce a series of random numbers from 1 to 6). One way to generate pseudo random numbers from the uniform distribution is using the ' ...

...users click on what appears as the first search results and it is unlikely to click on results that do not appear at the beginning, even though relevant. ...el'' of user behavior, assumes that the user scans search results from top to bottom and eventually stops because either their information need is satisf ...

...osal generally cannot model shared information between groups. One idea is to make <math>G_0</math> become discrete by limiting the choice of <math> G_0 ...e measure. Note that <math>G_0</math> is discrete with probability one due to the fact of Dirichlet process. ...

...s that it is computationally expensive. Many algorithms have been proposed to solve the sparse PCA problem, and the authors introduced a fast block coord ...nsion of the data. Since <math>\hat{n}</math> could be very small compared to the dimension <math>n</math> of the data, this algorithm is computationally ...

...s with computing k-nearest neighbors of each input and adding a constraint to preserve distances and angles between k-nearest neighbors:<br /> and also a constraint on outputs to be centerd on the origin:<br /> ...

...easy to train and easy to generalize, while neuroscientists' objective is to produce useful representation of the scientific data. In other words, machi ...e at 1/2 of their maximum rate when at zero. A solution to this problem is to use a rectifier neuron which does not fire at it's zero value. This rectifi ...

...based methods where they learn the i-th training examples are "remembered" to learn for corresponding weights. Prediction on untrained examples are then ...nal feature space and then apply existing linear methods. The main goal is to reduce the bottleneck of kernel-based inference methods. ...

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...o random variables could be in different dimensions. Second, dCov is equal to zero if and only is the two variables are independent. ...ritten in terms of the expectations of Euclidean distances which is easier to interpret: ...

...stics and signal processing. Except in some special cases the RMP is known to be computationally hard. \mbox{subject to: } & X \in C, ...

...rger. Furthermore, having many layers can theoretically cause problems due to vanishing gradients. ...number of input regions. This is caused by the deep hierarchy which allows to apply the same computation across different regions of the input space. ...

..."bank" as a "financial institution" or the "land alongside or sloping down to a river or lake". ...e positional encoding, which has the same dimension as the word embedding, to obtain the sequential information of the inputs. BERT is built by the N uni ...

...f pneumonia in CT Scan images. Then they carried out 10 k cross validation to estimate the model will perform on unseen dataset. And finally they evaluat ...iologists, radiologists and computer scientists have been working together to detect microbial diseases such as tuberculosis, malaria and pneumonia using ...

...sform Method and sample from independent uniform distributions seen before to generate a sample following a Gamma distribution. ...le to use the Acceptance-Rejection method, but there are still better ways to sample from a Standard Normal Distribution. ...

...imal). This creates a big problem, as this method becomes very susceptible to poor data (i.e., not very robust). This intuitively makes sense, as the age ...e noisy demonstration to be ranked according to their relative performance to each other. Another similar method requires extra labelling of the data wit ...