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  • The projection pursuit concept was developed by Jerome H. Friedman and John Tukey in 1974. ...x to obtain a subspace of dimension <math>k_{0}</math>. The value of <math>h</math> is chosen as ...
    15 KB (2,414 words) - 09:46, 30 August 2017
  • A valid hash function <math>h</math> must satisfy the property Pr[h(x_i)= h(x_j)] = sim(x_i, x_j) ...
    17 KB (2,894 words) - 09:46, 30 August 2017
  • <math> \hat{Q}(h(S), v;\Theta) = \theta_5^{T} relu([\theta_6 \sum_{u \in V} \mu_u^{(T)}, \th <math>r(S,v) = c(h(S'),G) - c(h(S),G);</math> ...
    12 KB (1,976 words) - 23:37, 20 March 2018
  • ...\times H} </math>, where the size of the image is <math>3 \times W \times H</math> as the preturbation. In this case, <math>Dissim(\delta)=0 </math>. ...nels of a pixel are not equal and it uses <math> \delta_{3 \times W \times H} </math> with the <math>Dissim(\delta) = || \delta_{R}- \delta_{B}||_p + | ...
    15 KB (2,325 words) - 06:58, 6 December 2020
  • ...\alpha)</math> is defined using two parameters. The first parameter, <math>H</math>, is a base distribution. This parameter can be considered as the mea <math>\, \theta_k</math>~<math>\, H</math> ...
    12 KB (2,039 words) - 09:46, 30 August 2017
  • ...opposed to computing the inner product. Denoting the weak classifiers by $h(\cdot)$, we obtain the strong classifier as: H(x_i) = \sum\limits_{j = 1}^K \alpha_j h(x_{ij}; \lambda_j) ...
    21 KB (3,321 words) - 15:00, 4 December 2017
  • where |Ω| is the size of the data set, H<sub>n</sub> is the nth chunklet, |H<sub>n</sub>| is the size of the nth chunklet, and N is the number of chunkl ...ximize the entropy of Y, H(Y). This is because I(X,Y) = H(Y) – H(Y|X), and H(Y|X) is constant since the transformation is deterministic. Intuitively, si ...
    21 KB (3,516 words) - 09:45, 30 August 2017
  • a_{t} =h_{t-1}^{cat} W^h + b^h \hspace{2cm} (2) <math>W^h∈R^{(R+M)\times M} </math> guarantees each hidden state provided by the prev ...
    25 KB (4,099 words) - 22:50, 20 April 2018
  • Bilen, H., and Vedaldi, A. 2017. Universal representa- tions: The missing link betwe Rebuffi, S.-A.; Bilen, H.; and Vedaldi, A. 2017. Learning multiple visual domains with residual adap ...
    10 KB (1,371 words) - 00:44, 14 November 2021
  • ..., which are then multiplied by the weight matrix <math>w_h</math> in <math>h</math> to produce the output logits as shown in Figure 1. ...
    10 KB (1,573 words) - 23:36, 9 December 2020
  • ...<math>g \,</math> and <math>h \,</math>, <math>g(y_i) \,</math> and <math>h(y_j) \,</math> are uncorrelated. ...possible values <math>\{x_1, x_2, ..., x_n\} \,</math> is defined as <math>H(X) = -\sum_{i=1}^n {p(x_i) \log p(x_i)}</math> ...
    15 KB (2,422 words) - 09:45, 30 August 2017
  • ...finite sequences of words in the source and target language, and let <math>H'</math> denote the set of finite sequences of vectors in the latent space. ...s a sequence of hidden states <math display="inline">(h_1,\ldots, h_m) \in H'</math> in the latent space. Crucially, because the word vectors of the tw ...
    28 KB (4,522 words) - 21:29, 20 April 2018
  • ...to the largest singular value of A. Therefore, for a linear layer <math> g(h)=Wh </math>, the norm is given by <math> ||g||_{Lip}=\sigma(W) </math>. Obs ...ator more sensitive, one would hope to make the norm of <math> \bar{W_{WN}}h </math> large. For weight normalization, however, this comes at the cost of ...
    16 KB (2,645 words) - 10:31, 18 April 2018
  • ...The authors define the deconfusing function as an indicator function <math>h(x, y, g_k) </math> which takes some sample <math>(x,y)</math> and determine $$ R(g,h) = \int_x \sum_{j,k} (f_j(x) - g_k(x))^2 \; h(x, f_j(x), g_k) \;p(f_j) \; p(x) \;\mathrm{d}x $$ ...
    27 KB (4,358 words) - 15:35, 7 December 2020
  • stochastic binary feature vector <math> \mathbf h </math> are modeled by products of conditional Bernoulli distributions: <br> <center> <math> \mathbf p(x_i=1|h)= \sigma(b_i+\sum_{j}W_{ij}j_j) </math> </center> ...
    20 KB (3,263 words) - 09:45, 30 August 2017
  • Let <math>({ H}_1, k_1)</math> and <math>({H}_2, k_2)</math> be RKHS over <math>(\Omega_1, { B}_1)</math> and <math>(\Om <math><f, \Sigma_{YU}g>_{{H}_1} \approx \frac{1}{n} ...
    14 KB (2,403 words) - 09:45, 30 August 2017
  • ...purpose of the latent vector is to model the conditional distribution $p(x|h)$ such that we get a probability as to if the images suites this descriptio $$p(x|h) = \prod\limits_{i=1}^{n^2} p(x_i | x_1, ..., x_{i-1}, h)$$ ...
    31 KB (4,917 words) - 12:47, 4 December 2017
  • <math> E\left(\mathbf{v}, \mathbf{h}; \mathbf{W}\right) = - \sum_{i \in visible}a_iv_i - \sum_{j \in hidden}b_j * <math>\mathbf{h}</math> is the vector of hidden units, with components <math>h_j</math> and ...
    24 KB (3,699 words) - 09:46, 30 August 2017
  • :<math>PP(p) := 2^{H(p)}=2^{-\sum_x p(x)\log_2 p(x)}</math> Here <math>H(p)</math> is the entropy in bits and <math>p(x)</math> is the probability o ...
    13 KB (2,144 words) - 05:41, 10 December 2020
  • h <math>(x, y)</math> and <math>(w, h)</math> are normalized to the range <math>(0, 1)</math>. Further, <math>p_c ...
    19 KB (2,746 words) - 16:04, 20 November 2018
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