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Dg[W](H)= H^T W + W^T H. D^\ast g[W](H)= WH^T +WH. ...

# $\bar{h} = \frac{1}{T_x}\sum\limits^{T_x}_{l=1}h_l$ # $𝜇_{CC} = f_{CC}([\bar{c_{t}},\bar{h}])$ ...

<br><math>H\left({\boldsymbol{\alpha} }\right)=\frac{1}{N}\sum^N_{n=1}{F\left({{\mathbf ...g to make the solution sparse. The learning algorithm is to minimize <math>H\left({\boldsymbol{\alpha} }\right)</math> with respect to <math>{\boldsymbo ...

H &= tanh(W_xX + (W_gg)𝟙^T)\\ a_x &= softmax(w_{hx}^T H)\\ ...

...rying k with different hidden unit sizes <math>h</math> by keeping <math>k*h</math> or a similarly related term constant. This is better studied in [5] # Speech and Language Processing. Daniel Jurafsky & James H. Martin. 2017. Draft of August 28, 2017. ...

...f dimensions h x w, a stacked hourglass (Appendix 2) is used to generate a h x w x f representation of the image. It should be noted that the dimension ...

<center><math> \frac{H}{\theta} = \frac{T}{1-\theta} </math></center> \begin{center} H = \# of all <math>x_i = 1</math>, e.g. \# of heads <br /> ...

...the model, the observed points are encoded using a three-layer MLP encoder h with a 128-dimensional output representation. The representations are aggre of the encoder h to include convolution layers as ...

...ion. We use this to solve an integral of the form: <math> I = \int_{a}^{b} h(x) dx </math> \displaystyle I & = \int_{a}^{b} h(x)dx \\ ...

Lee, H., Battle, A., Raina, R., and Ng, A.Y. Efficient Lee, H., Chaitanya, E., and Ng, A. Y. Sparse deep belief ...

...minimize here during training is <math>E=-\sum_a\sum_{k=1}^{C}{y_{n,k}log(h{n,k})}</math>, where <math>n</math> denotes the training example, and <math ...

...ion problem is generally NP-hard<ref name="fazel2004">Fazel, M. and Hindi, H. and Boyd, S. Rank minimization and applications in system theory. Proceedi ...ine Learning Research'', 7:2541-2563, 2006.</ref> and Zou<ref name="Z2006">H. Zou. The adaptive lasso and its oracle properties. ''Journal of the Amer ...

[2] Y. Song, J. Huang, D. Zhou, H. Zha, and C. L. Giles, “IKNN: Informative K-nearest neighbor pattern classi [12] Z. H. Zhou and Y. Yu, “Ensembling local learners throughmultimodal perturbation, ...

3. Dulac-Arnold, G.; Evans, R.; van Hasselt, H.; Sunehag, P.; Lillicrap, T.; Hunt, J.; Mann, T.; Weber, T.; Degris, T.; an 6. VanHasselt,H.,andWiering,M.A. 2009. Usingcontinuousactionspacestosolvediscreteproblems. ...

:<math>\begin{align}I &= \displaystyle\int_a^b h(x)\,dx :<math>\displaystyle w(x) = h(x)(b-a)</math> ...

...e distributed data fusion technique, Channel Filter <ref> A. Makarenko and H. Durrant-Whyte, “Decentralized Bayesian algorithms for active sensor networ ...

Use the cluster membership <math>H=(h_i^k) </math> obtained to reconstruct the K centres <math>C_{\mu}^* = [ \ ...

...y but all three have the same fundamental idea. This is given by <math>2^{{H(p)}}=2^{{-\sum _{x}p(x)\log _{2}p(x)}} </math> Suppose you have a four-side ...of input elements. The output of l-th block of decoder is denoted by <math>h^l = (h_1^l,....,h_n^l)</math> and <math>z^l = (z_1^l,....,z_m^l)</math>. Ea ...

\min_{u \in \mathbb{R}^n} \max_{v \in \mathbb{R}^m} \ u^T P v -H(v) + H(u) \\ where H(y) is the Gibbs entropy <math> \sum_i y_i log y_i</math>. ...

To avoid overfitting, the authors add causal entropy <math>−H (\pi_{\theta}) </math> as the regularization term. Thus, the learning objec \[\min_{\theta}\mathcal{L}=-\eta(\pi_{\theta})-\lambda_{2}H(\pi_{\theta})+\lambda_{1} \sup_{{D\in(0,1)}^{S\times A}} \mathbb{E}_{\pi_{\ ...