[ (v) 14.9989 (elop) -246.98 (a) -247.004 (ne) 24.9876 (w) -246.992 (frame) 25.0142 (w) 8.99108 (ork) -245.982 (for) -247 (higher) -246.98 (order) -247.004 (CRF) -247.014 (inference) -246.98 (for) ] TJ /Filter /FlateDecode 0.989 0 0 1 50.1121 296.193 Tm /Count 11 0 scn [ (guarantees) -254.01 (are) -254.005 (hardly) -252.997 (pro) 14.9898 (vided\056) -314.998 (In) -254.018 (addition\054) -254.008 (tuning) -253.988 (of) -252.982 (h) 4.98582 (yper) 19.9981 (\055) ] TJ >> << [ (sical) -275.99 (methods) -276.016 (ha) 20.0106 (v) 14.9989 (e) -275.987 (e) 14.0067 (xponential) -276.021 (dependence) -275.017 (on) -275.987 (the) -275.982 (lar) 16.9954 (gest) ] TJ [16] Misha Denil, et al. (\054) Tj /R21 38 0 R 1 0 0 1 530.325 514.469 Tm ET [ (or) 36.009 (der) -263.005 (potenti) 0.99344 (als\056) -357.983 (In) -262.012 (this) -262.981 (paper) 108.996 (\054) -267.983 (we) -262.012 (show) -262.99 (that) -262.997 (we) -263.011 (can) -262.982 (learn) ] TJ 0.983 0 0 1 308.862 164.686 Tm Q BT 0.6082 -20.0199 Td T* In this paper, we propose a framework called GCOMB to bridge these gaps. /R12 9.9626 Tf ET 11.9551 TL 1.02 0 0 1 308.862 478.604 Tm [5] [6] use fully convolutional neural networks to approximate reward functions. 10 0 0 10 0 0 cm /R10 23 0 R 4.60703 0 Td 0.996 0 0 1 308.862 406.873 Tm [ (in) -251.016 (a) -249.99 (series) -250.989 (of) -249.98 (w) 9.99607 (ork\054) -250.998 (reinforcement) -250.002 (learning) -250.998 (techniques) -249.988 (were) ] TJ >> Get the latest machine learning methods with code. 0.98 0 0 1 50.1121 188.597 Tm 0.98 0 0 1 50.1121 236.417 Tm 1.006 0 0 1 308.862 116.866 Tm /Font 301 0 R << 1.014 0 0 1 365.805 382.963 Tm Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). Q 1.015 0 0 1 62.0672 212.507 Tm (5) Tj BT q ET [ (tion) -282.986 (remain\056) -416.985 (Those) -282.995 (inconsistencies) -282.004 (can) -283.003 (be) -283.015 (addressed) -283.015 (with) ] TJ /ColorSpace 482 0 R 1.02 0 0 1 308.862 104.91 Tm /R12 9.9626 Tf [ (are) -247.006 (heuristics) -246.991 (which) -247.988 (are) -247.006 (generally) -247.004 (computationally) -247.991 (f) 10.0172 (ast) -246.989 (b) 19.9885 (ut) ] TJ Q 1.02 0 0 1 540.288 514.469 Tm 0 1 0 scn Q 1.02 0 0 1 474.063 514.469 Tm f ET BT Q BT q [ (Conditional) -239.997 (Random) -240.006 (Fields) -239.986 (\050CRFs\051\054) -244.002 (albe) 1.01274 (it) -240.986 (requiring) -239.991 (to) -239.998 (solv) 15.016 (e) ] TJ (\054) Tj q h 73.895 23.332 71.164 20.363 71.164 16.707 c (85) Tj /Resources << 3 0 obj /Resources << /Parent 1 0 R >> stream The comparison of the simulation results shows that the proposed method has better performance than the optimal power flow solution. In the simulation part, the proposed method is compared with the optimal power flow method. [ (Can) -250.003 (W) 65.002 (e) -249.999 (Lear) 14.9893 (n) -249.99 (Heuristics) -250.013 (F) 24.9889 (or) -249.995 (Graphical) -249.993 (Model) -249.986 (Infer) 18.0014 (ence) -250.007 (Using) -249.991 (Reinf) 25.0059 (or) 17.9878 (cement) ] TJ 10 0 0 10 0 0 cm /Font 476 0 R 10 0 0 10 0 0 cm [ (Program) -316.003 (\050ILP\051) -316.016 (using) -315.016 (a) -316.004 (combination) -315.992 (of) -315.982 (a) -316.004 (Linear) -315.002 (Program\055) ] TJ endobj Published as a conference paper at ICLR 2020 LEARNING DEEP GRAPH MATCHING VIA CHANNEL- INDEPENDENT EMBEDDING AND HUNGARIAN ATTEN- TION Tianshu Yu y, Runzhong Wangz, Junchi Yanz, Baoxin Li yArizona State University zShanghai Jiao Tong University ftianshuy,baoxin.lig@asu.edu frunzhong.wang,yanjunchig@sjtu.edu.cn We introduce a fully modular and Q ET [ (programs) -300.982 (is) -300.005 (computationally) -301.018 (e) 15.0061 (xpensi) 25.003 (v) 14 (e) -300.012 (and) -301 (therefore) -299.998 (pro\055) ] TJ /R21 cs 1 0 0 1 308.862 347.097 Tm q ET [ (optimization) -254.004 (task) -253.991 (for) -254.013 (robotics) -254.016 (and) -254.006 (autonomous) -254.019 (systems\056) -316.986 (De\055) ] TJ Abstract. 96.422 5.812 m /R18 9.9626 Tf /R9 cs /R9 cs q 10 0 0 10 0 0 cm Q At KDD 2020, Deep Learning Day is a plenary event that is dedicated to providing a clear, wide overview of recent developments in deep learning. << [ (tion\054) -226.994 (pr) 46.0032 (o) 10.0055 (gr) 15.9962 (ams) -219.988 (ar) 38.0014 (e) -219.995 (formulated) -218.995 (for) -220.004 (solving) -220.004 (infer) 38.0089 (ence) -218.999 (in) -219.994 (Condi\055) ] TJ /Type /Page -102.617 -37.8578 Td /R12 9.9626 Tf /ColorSpace 133 0 R Q 0 scn /R14 8.9664 Tf (93) Tj 0.98 0 0 1 308.862 538.38 Tm /Contents 310 0 R Using deep Reinforcement learning and access state-of-the-art solutions via deep Reinforcement learning ” and Azalia Mirhoesini ; Differentiable Physics-informed networks. 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