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|Oct 31 || ||6 || || || ...

L = L_{CE} + L_{Reg} + \lambda \times L_{MR} \tag{6} \label{eq:op5} <div align="center">'''Figure 6:''' Analyzing Co-Exitation</div> ...

==Project 6: Dimensionality Reduction for Supervised Learning == ...arge-scale noisy anchor-free graph realization. SIAM J. on Sci. Comp., 31, 6, 4351-4372. </ref>. ...

...into three categories: (a) appropriately selecting the neighbors [2] [5] [6] [7] [10]; (b) identifying the outliers [4] [8]; (c) dealing with the insta 6. Optional: extend the analysis to other financial time series, e.g. GDP, un ...

...; margin-left: auto; margin-right: auto;">[[File:Screen Shot 2018-11-10 at 6.03.08 PM.png|400px]]</div> ...layers directly to the deep layers are coming from networks like ResNet [6] ...

...le 1. The baseline models are D-MTAE[5],Deep-All (Vanilla AlexNet)[2], DSN[6]and AlexNet+TF[2]. On average, the proposed method outperforms other method ...ctors – pole length and cart mass. In both experiments, we randomly choose 6 source domains for training and hold out 3 domains for (true) testing. Sinc ...

| No conv, 6 full ...3-5% relative improvement over Hybrid DNN. Also CNN-based feature offers 5-6% relative improvement over DNN-based features. ...

...eta}</math> we will get the factorized matrix <math>X\approx QYQ^T</math> (6)<br /> First, starting from the m-dimensional solution of eq. (6), use conjugate gradient methods to maximize the objective function in eq. ...

...ained InceptionV3 network where all layers except the last one are frozen [6] (Footnote). Adding only one poison instance to the training set causes mis [6] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigni ...

...ation of two entities in the same sentence into 6 potential relations. The 6 relations are USAGE, RESULT, MODEL-FEATURE, PART WHOLE, TOPIC, and COMPARE. ...ioned 9 natural language relationships between the word pairs. Among them, 6 potential relationships are USAGE, RESULT, MODEL-FEATURE, PART WHOLE, TOPIC ...

...ionary (DND) used in Neural Episodic Control (NEC) found in [[#References|[6]]], though the gradients from the memory in the MbPA model are not used dur * ^[6]Pritzel. Alexander, Uria. Benigno, Srinivasan. Sriram, Puigdome ...

...le, "AU 1" stands for the inner portion of the brows being raised, and "AU 6" stands for the cheeks being raised. Such a framework helps in describing a ...hich recognizes both permanent and transient AUs with high accuracy rates [6]. Hand-crafted feature descriptors like the LBP are very powerful facial re ...

:<math> X = \{0, 1, 2, 3, 4, 5, 6, 7, 8\} \rightarrow</math>'''State Space''' ...

6. Both natural and random images are found to be vulnerable to adversarial p The result is summarized in Figure 6: ...

...\max_{x_k} \psi(x_j,x_k) \phi(x_k,y_k) \prod_{i!=j} \hat{M}^l_k \,\,\, (6) ...ration uses the messages above as the <math>\hat{M}</math> variables in Eq(6) : ...

...es. Particularly, in the car, person, and rider categories, a 12%, 7%, and 6% higher performance than SharpMask is achieved. File:Figure_3_Neel.JPG|Figure 6: Qualitative results: comparison with human annotator.|alt=alt language ...

...m \; L(y_{ti} , <w_t,x_ti)>) + \gamma \sum_{t=1}^T \; <w_t,D^+w_t> \;\;\; (6)</math></center> ...d in the Reference section) that the function <math>\,R</math> in <math>\,(6)</math> is jointly convex in both <math>\,W</math> and <math>\,D</math>. ...

...rpretations directly in the models, often known as self-explaining models [6, 7, 8, 9]. The alternative option is to generate interpretations in post-ho [6] David Alvarez-Melis and Tommi S Jaakkola. Towards robust interpretability ...

...t reached an accuracy of 72%. Reducing the size of the training dataset to 6 billion caused lower accuracy (66%), which suggests that large amount of th Table 6 shows the empirical comparison between different neural network-based repre ...

...ons are shown to useful for language learning[2]. Several studies[3][4][5][6] have shown that feedback is especially useful in second language learning ...s right”. In the datasets, there are 6 templates for positive feedback and 6 templates for negative feedback, e.g. ”Sorry, that’s not it.”, ”Wrong”, etc ...