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...layer output matrix <math>\mathbf{H}</math> of ELM is given as: <math>{\bf H}=\left[\begin{matrix} {\bf h}({\bf x}_1)\\ ...

...{x})}}[E(\mathbf{x})]- E_{\mathbf{x} \sim q(\mathbf{x})}[E(\mathbf{x})] + H(q) ...lity was used to obtain the variational lower bound on the NLL given <math>H(q) </math>. This bound is tight if <math> q(x) \propto e^{-E(\mathbf{x})} \ ...

.../filter size to be 4*H and the number of attention heads to be H/64 (where H is the size of the hidden layer). Next, we explain the changes that have be ...which usually is harder. However, if we increase <math display="inline">\\H</math> and <math display="inline">\\E</math> together, it will result in a ...

variables H in addition to X, with the Markov chain state (and mixing) involving both X and H. Here H is the angle about ...

...is the <math>0^{\text{th}}</math> layer and the output layer is the <math>H^{\text{th}}</math> layer). The input <math>X</math> is a vector with <math> ...dom network output <math>Y</math> is <math>Y = q\sigma(W_H^{\top}\sigma(W_{H-1}^{\top}\dots\sigma(W_1^{\top}X)))\dots),</math> where <math>q</math> is a ...

...selects whether the hidden state is to be updated with a new hidden state h˜. The reset gate r decides whether the previous hidden state is ignored. ]] ::<math> r_j=\sigma([\mathbf{W}_r\mathbf{x}]_j+[\mathbf{U}_r\mathbf{h}_{t-1}]_j )</math> <br/> ...

The vocabulary is represented by a matrix <math> \mathbf{E}\in \mathbb{R}^{h \times v} </math> with a look up layer, denoted by the embedding <math> e_\ ...define the local context unit <math> \mathbf{K}_{\omega_i}\in \mathbb{R}^{h\times\left (2c+1\right )}</math>. Let <math> \mathbf{K}_{\omega_i,t} </math ...

...ight matrix from the projection layer to the hidden layer and the state of H would be: <math>\,h=tanh(Ha + b)</math> where A is the concatenation of all <math>\,a_i</math> ...

...ode G which passes the ball to nodes I & D. Node F passes the ball to node H which passes the ball to the already visited node, I. Therefore all nodes a H ...

...dentically distributed), <math>X</math> and associated hidden labels <math>H</math> are generated by the following model: $$P(X, H) = \prod_{i = 1}^N P(X_{i,1}, \dots , X_{i,N_i}| H_i)P(H_i) \quad \quad \ ...

...ngle af+bg,h\rangle=a\langle f,h\rangle+b\langle g,h\rangle,\,\forall\,f,g,h\in\mathcal{F}</math> and all real <math>\,\!a</math> and <math>\,\!b</math> ...f\otimes g)h:=f\langle g,h\rangle_{\mathcal{G}} \quad</math> for all <math>h\in\mathcal{G}</math> ...

<math>(f\otimes g)h:=f<g,h>_\mathcal{G}</math> for all <math>h\in \mathcal{G}</math> where <math>H,K,L\in \mathbb{R}^{m\times m},K_{ij}:=k(x_i,x_j),L_{i,j}:=l(y_i,y_j) and H_ ...

[3] H. B. McMahan. Follow-the-regularized-leader and mirror descent: Equivalence [4] T. Mikolov, A. Deoras, D. Povey, L. Burget, and J. H. Cernocky. Strategies for training large scale neural network language mode ...

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 ...

A valid hash function <math>h</math> must satisfy the property Pr[h(x_i)= h(x_j)] = sim(x_i, x_j) ...

...lc|name|a|b|c|d|e|f|g|h|i|j}}</nowiki></code> <td>{{tlc|name|a|b|c|d|e|f|g|h|i}} <td>Shows up to eight parameters. The rest are dropped. ...

<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> ...

...\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 + | ...

...\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> ...

...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) ...