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...comes from expanding the solution of the original problem in terms of the bottom eigenvectors of a graph laplacian. As the smaller SDPs coming from this fac <math>\,\min_{x_1,...,x_n}\Sigma_{i\sim j}{(\|x_i-x_j\|^2-d_{ij}^2)^2}</math> (1)<br /> ...

...the decoding strategies impact machine text. the author For example in the figure below, the GPT2 model tries to generate the continuation text given the con [[File: GPT2_example.png |caption=Example text|center |800px|caption position=bottom]] ...

[[File:figure_1_bengio.png |thumb|upright=1.75| Figure 1 Top: <ref>Bengio, Yoshua, Mesnil, Gregoire, Dauphin, Yann, and ´ Bottom: More generally, a GSN allows the use of arbitrary latent ...

...diagram of the difference in the architecture can be seen in the following figure. ...on (x-axis) and downsized to 32 x 32 (y-axis).|(Odena et al., 2016) Figure 2: (Left) Inception accuracy (y-axis) of two generators with resolution 128 x ...

...uction of the original input. This is analogous to learning a data-driven "bottom-up" representation of the input that is used to inform the higher-order "to [[File:Network.png |frame | center |Figure 1: The Helmholtz network structure. ]] ...

2) Visual relationship detection methods like message passing RNNs and predic 2) Vector embeddings to detect body joints of the various people in an image. ...

...prediction performance of methods is coefficient of determination (<math>R^2</math>). ...values of one or two parameters at a time, and then calculate the <math>R^2</math> for DNNs trained with the selected parameter settings. These results ...

...(p,q) \epsilon R_{ij}} (a_{kpq})</math> with everything defined as before. Figure 1 provides a numerical example that can be followed. ...he data is averaged with values of significantly lower intensities. Figure 2 displays an image of this. ...

...This is done by randomly nullifying features within images. Tramer et al [2], showed the state-of-the-art Ensemble Adversarial Training Method, which a [[File:non-targeted O.JPG| 600px|center]] ...

(2) What set of parameters, <math display="inline"> \vec{\lambda} </math>, bes ...iation is accomplished using a technique called automatic differentiation [2]. Importantly, the weights of the two neural networks will be shared, since ...

...esentations_for_Efficient_Architecture_Search#Primitive_operations section 2.3] are used to form small networks defined as ''motifs'' by the authors. To ...its bottom and only one complex motif at its top. Any motif in between the bottom and top levels can be defined as the composition of motifs in lower levels ...

...} J=\sum^K_{k=1}\sum_{\mathbf x \in C_k}\|\mathbf x - \boldsymbol{\mu}_k\|^2</math> ...math>\mathop{\min_{\mathbf Y}}K-tr(\mathbf{Y^{\rm T}(D^{\rm{1/2}}WD^{\rm{1/2}})Y})</math> ...

[[File:one-pixel-attack.jpg|500px|middle]] [[File:face2.jpg|200px|middle]] ...

2. Huang, Jingyue ...fication. It defines <math> region\left ( i,c\right ) </math> as the <math>2\times c+1</math> length region with middle word <math> \omega_i </math> whi ...

a_{t} =h_{t-1}^{cat} W^h + b^h \hspace{2cm} (2) [[File:StdRNN.png|650px|center||Figure 1: Recurrent Neural Network]] ...

2. The second idea is to train the system to not only produce a distribution .../en.wikipedia.org/wiki/Information_retrieval#Mean_average_precision mAP]). Figure 1 illustrates the higher difficulty of the detection process. ...

The following figure shows the network used to model the joint distribution The figure below shows a hybrid network where the top two layers have undirected conne ...

...be a random vector on <math>\,\Omega_{11}\times \Omega_{12} \times \Omega_{2}</math>, where <math>\,X = (U,V)</math>, and let <math>\,H_1 = H_{11} \otim ...decaying, as <math>\,W_{ij} = exp \left(\frac{- \|x_i - x_j \|^2}{\sigma^2}\right) </math>. Let D denote the diagonal matrix with elements <math>\, D_ ...

..._j ||^2/ 2\sigma_i ^2 )}{\sum_{k \neq i} \exp(-||x_i-x_k ||^2/ 2\sigma_i ^2 ) }</math> </center> ...q_{j|i} = \frac{\exp(-||y_i-y_j ||^2)}{\sum_{k \neq i} \exp(-||y_i-y_k ||^2) }</math> </center> ...

<math> \mathbf {E(F,G) = \|X-FG^T\|^2}</math> <math> J_{K-means} = \sum_{i=1}^n \sum_{k=1}^K g_{ik}||x_i-f_k||^2=||X-FG^T||^2 </math> ...

In this paper, they trained a large, deep neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has roughly 1.2 million labeled high-resolution training images, 50 thousand validation ima ...

...and transferred as a model that is likely to work on a new source domain [2]. Finally, a domain-invariant feature representation is learned to minimize [[File:ashraf1.jpg |center|600px]] ...

2. Planning and acting using that map. ...View Images and a dual pathway agent architecture is designed. As shown in figure 1, some parts of the environment are built using Google StreetView images o ...

<div align="center">'''Figure 1:''' Object Detection on an image</div> Figure 1 shows an example where the model identifies and locates all the instances ...

[[File: Whole Slide Tissue Image of a grade IV tumor.png|thumb|300px|upleft|Figure 1: Whole Slide Tissue Image of a grade IV tumor. Features indicating subtyp ...3]. These methods excel when an abundance of patch labels are provided [1, 2], allowing patch-level supervised classifiers to learn the assortment of ca ...

...parallelize the training process. In this work, Oord et al. [[#Reference|[2]]] introduced a Gated PixelCNN, which is a convolutional variant of the Pix ...el is a simple generative method wherein given an image of dimension $x_{n^2}$, we iterate, employ feedback and capture pixel densities from every pixel ...

...or meta-learning methods that learn an update function or learning rule [1,2,3,4], this algorithm does not expand the number of learned parameters nor p [[File:model.png|200px|right|thumb|Figure 1: Diagram of the MAML algorithm]] ...

...ange vision for autonomous off‐road driving." Journal of Field Robotics 26.2 (2009): 120-144.</ref>; to robustly increase the obstacle and path detecti ...issues, a normalized “pyramid” containing 7 sub-images are extracted (see figure above), where the top row of the pyramid has a range from 112 meters to inf ...

...t varies based on colour, texture, lighting, background, etc. However, the 2.5D sketches (e.g. depth or normal maps) of the object remain constant, and ...n the idea from [Marr, 1982] that human 3D perception relies on recovering 2.5D sketches, which include depth maps (contains information related to the ...

...u - \log v - 1 + \nu 1 \\ 1^T u - 1 \\ 1^T v - 1 \\ \end{bmatrix}, \qquad (2) 2. For small changes du, dv, ...

...are models that use Hidden Markov Models [25] or Mixture Density Networks [2] to generate human sketches, continuous data points (modelling Chinese char ...classes, each class has 70k training samples, 2.5k validation samples, and 2.5k test samples. ...

[[File:1-GSP.png | 650px|thumb|center|Figure 1: The goal-conditioned skill policy (GSP) takes as input the current and g ...termines whether the goal has been satisfied with respect to some metrics. Figure 1 shows various GSPs along with diagram (d) showing the forward-consistent ...

...utputs. The results of OGD-AVE, ODG-GTL, OGD-ALL are compared to SGD, ECW [2], (a regularization method using Fischer information for importance weights ...sk is a fixed permutation that gets applied to each MNIST digit. The below figure shows the performance comparison of different methods when applied on the p ...

[[File:001.jpg|300px|center]] 2. A CNP is permutation invariant in <math display="inline">O</math> and <mat ...

...at the top level (manager) produces a meaningful and explicit goal for the bottom level (worker) to achieve. ...ds using auxiliary losses and rewards such as pseudo count for exploration[2] have significantly improved results by stimulating agents to explore new p ...

* 2 different pairs Consider the Lemonade Stand example from Figure 1 Below. We have 4 players and the goal for each player is to find a spot i ...

...ndamental idea. This is given by <math>2^{{H(p)}}=2^{{-\sum _{x}p(x)\log _{2}p(x)}} </math> Suppose you have a four-sided dice (not sure what that’d be) The following figure depicts the underlying architecture: ...

'''2. Filtering''' The weights have 2 main models used Boolean model and TF-IDF model: ...

In the first figure (Fig. 21) we have no information about the node Y and so we can not say if ...raphs. Here the Bayes ball rule is simpler and more intuitive. Considering Figure.... , a ball can be thrown either from x to z or from z to x if y is not ob ...

i.e. From <math>x \sim~f(x)</math> sample <math>\,x_{1}, x_{2}, ..., x_{1000}</math> for ii = 2:1000 ...

...chers from Cornell University, collected 509 million Twitter messages from 2.4 million users in 84 different countries. The data they used were words co 002: T/Th 1:00-2:20pm DC1351 <br /> ...

The figure below demonstrates an example of PCA. Data is transformed from original 3D Image:PCA 2.png|Extraction of the principal components ...

...ally distributed (i.i.d)] ordered pairs <math>\,\{(X_{1},Y_{1}), (X_{2},Y_{2}), \dots , (X_{n},Y_{n})\}</math>, where the values of the <math>\,ith</mat [[File:Data1.jpg]] ...