Search results

Manager and Worker are recurrent networks (<math>{h^M}</math> and <math>{h^W}</math> being their internal states). <math>\phi</math> is a linear trans ...ed by the following equations: <math>\hat{h}_t^{t\%r},g_t = LSTM(s_t, \hat{h}_{t-1}^{t\%r};\theta^{LSTM})</math> where % denotes the modulo operation an ...

To create a common embedding, every image is represented by a set of h-dimensional vectors <math> \{v_i | i = 1 ... 20\}</math> where each <math ...fully connected layer. The matrix <math> W_m </math> has dimension <math> h \times 4096</math>. ...

[12] J. H. Friedman. Greedy function approximation: a gradient boosting machine. Anna [13] J. H. Friedman. Stochastic gradient boosting. Computational Statistics & Data An ...

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layerwise Larochelle, H., Erhan, D., Courville, A., Bergstra, J., & Bengio, Y. (2007). ...

...y each encoder model is then concatenated into a single hidden state <math>h</math>. ...ightarrow(S), h_\leftarrow = \text{encode}_\leftarrow(S_{\text{reverse}}), h=[h_\rightarrow; h_\leftarrow] ...

...problem, let <math>\mathbf M_S=\mathbf {HH^T}</math> and <math>\mathbf {Q=H^TW}</math>, we get:<br> ...n Q-((H^T)^{-1}Q)^T M_D (H^T)^{-1}Q)=\min_W Trace(Q^T I_n Q-Q^TH^{-1} M_D (H^{-1})^T Q)}</math><br> ...

...math>C</math> dimensional representation, where <math>w </math> and <math>h </math> are the spatial dimensions of <math>x </math>, and the number of ch <math>H(q)</math>. <math>H(q)</math> is the entropy of the probability distribution over the symbols a ...

...> x_T^j </math>, which outputs the embedding vector <math> \overrightarrow{h^t_j} </math>, of size <math> d </math> for each bin <math> t </math> ...h> x_1^j </math>, which outputs the embedding vector <math> \overleftarrow{h^j_t} </math>, of size <math> d </math> for each bin <math> t </math> ...

...= \frac{1}{\sum_{(x,h) \in D_k} h} \displaystyle\sum_{(x,h) \in D_k, x<z} h,</math> [7] T. Chen, H. Li, Q. Yang, and Y. Yu. General functional matrix factorization using grad ...

filter(z, \delta) [i,j] = \frac{z[i,j]}{freq(w,h) [i,j]^\delta} mask(\lambda , g)[i,j] = \chi_{ top(\lambda w h, g g) } ...

kern(h,q) = \frac{1}{\epsilon + ||h-q||^2_2}. ...

...vectors are concatenated to form a vector <math>h</math>. The vector <math>h</math> is then projected to <math>\mu</math> and <math>\sigma</math> via t <math>\mu =W_\mu h + b\mu</math> ...

<math>a</math> is considered as a modulating factor and <math>h{(a,p)}=\frac{1}{ap+(1-a)} \in (0,1]</math> is a modulating function [1]. Th ...e because it could be larger than the softmax probability, while <math>p_m=h(a, p)*p < p </math> always holds. ...

...ResNet50x1, ResNet152x2 to the ViTs ViT-B/32, ViT-B/16, ViT-L/16, and ViT-H/14. The data used to train the models, unless specified, is the JFT-300M da * M. Naseer, K. Ranasinghe, S. Khan, M. Hayat, F. S. Khan, and M.-H. Yang. Intriguing properties of vision transformers, 2021. ...

...previous states, and the use of Echo State networks, <ref> Jaeger, H. and H. Haas. [http://www.sciencemag.org/content/304/5667/78.short "Harnassing Non ...essian of the cost function.In fact instead of computing and inverting the H matrix when updating equations, the Gauss-Newton approximation is used for ...

...e input. The classification rule used by a classifier has the form <math>\,h: \mathcal{X} \mapsto \mathcal{Y} </math>. ...mpirical error rate is the frequency where the classification rule <math>\,h</math> does not correctly classify any data input in the training set. In e ...

...n of the conformation problem formulation <ref name="bis"/> <ref>Leung N. H., and Toh K.-C. (2009) An SDP-based divide-and-conquer algorithm for large- ...d local tangent space alignment (LTSA) <ref name="zhan">Zhang, Z. and Zha, H. (2002) Principal manifolds and nonlinear dimension reduction via local tan ...

h(v_I,v_{I^t})=\frac{\exp \biggl( \frac{s(v_I,v_{I^t})}{\tau} \biggr)}{\exp \ ...{t})=-\text{log}[h(f(v_I),g(v_{I^t}))]-\sum_{I^{'}\in D_N}^{} \text{log}[1-h(g(v_{I^t}),f(v_{I^{'}}))] ...

...xtbf{P}} = [\textbf{P}^{[CLS]}_1,...,\textbf{P}^{[CLS]}_k] \in \mathbb{R}^{h \times k}</math>. Here <math> \textbf{w}_{start},\textbf{w}_{end},\textbf{w ...

<div align="center">Figure 2: Architecture of the 3-cluster APLC. h denotes the hidden state. Vh denotes the head cluster. V1 and V2 denote the [3] Jain, H., Prabhu, Y., and Varma, M. Extreme multi-label loss ...