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learning heuristics over large graphs via deep reinforcement learning

>> 0.996 0 0 1 308.862 406.873 Tm /R18 9.9626 Tf We will use a graph embedding network of Dai et al. 0.98 0 0 1 320.817 333.6 Tm 1.02 0 0 1 525.05 514.469 Tm q 1 0 0 1 370.826 382.963 Tm 100.875 9.465 l /Type /XObject 150.635 0 Td it is much more effective for a learning algorithm to sift through large amounts of sample problems. for quantified Boolean formulas through deep reinforcement learning. GCOMB trains a Graph Convolutional Network (GCN) using a novel probabilistic greedy mechanism to predict the quality of a node. 1 Introduction The ability to learn and retain a large number of new pieces of information is an essential component of human education. 10 0 0 10 0 0 cm [16] Misha Denil, et al. h /R12 9.9626 Tf 0.99 0 0 1 62.0672 308.148 Tm �WL�>���Y���w,Q�[��j��7&��i8�@�. 7 0 obj 4.60703 0 Td We ... Conflict analysis adds new clauses over time, which cuts off large parts of … /R9 cs /R9 cs 11.9563 TL 78.059 15.016 m /R9 cs We use the tree-structured symbolic representation of the GUI as the state, modelling a generalizeable Q-function with Graph Neural Networks (GNN). Additionally, a case-study on the practical combinatorial problem of Influence Maximization (IM) shows GCOMB is 150 times faster than the specialized IM algorithm IMM with similar quality. (\054) Tj 3 Problem De nition 1.007 0 0 1 50.1121 382.963 Tm ET 11.9547 TL /Type /Page Q 1.02 0 0 1 474.063 514.469 Tm T* 0.999 0 0 1 308.862 394.918 Tm 10 0 0 10 0 0 cm q /Contents 310 0 R ET /Parent 1 0 R q BT /MediaBox [ 0 0 612 792 ] 1.02 0 0 1 484.319 514.469 Tm BT /ExtGState 314 0 R /Resources 17 0 R (\054) Tj This novel deep learning architecture over the instance graph “featurizes” the nodes in the graph, which allows the policy to discriminate /Type /Page /R9 cs /Contents 42 0 R 0 scn >> q (82) Tj 0.98 0 0 1 308.862 538.38 Tm 16 0 obj [ (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 [ (construction) -251.014 (for) -251.012 (each) -251.015 (problem\056) -311.998 (Seemingly) -251.011 (easier) -250.991 (to) -250.984 (de) 24.9914 (v) 15.0141 (elop) ] TJ 1.014 0 0 1 390.791 382.963 Tm endobj f /Type /Catalog endobj 0 1 0 scn BT Q /R9 cs 0.98 0 0 1 50.1121 236.417 Tm 1 0 0 1 517.13 214.049 Tm Q 100.875 18.547 l endobj (1) Tj /ProcSet [ /PDF /ImageC /Text ] stream [ (Saf) 9.99418 (a) -249.997 (Messaoud\054) -249.993 (Magha) 19.9945 (v) -250.002 (K) 15 (umar) 39.991 (\054) -250.012 (Ale) 15 (xander) -249.987 (G\056) -250.01 (Schwing) ] TJ << 1.02 0 0 1 308.862 514.469 Tm /Subtype /Form /CA 0.5 >> /R21 cs /R12 9.9626 Tf 83.789 8.402 l BT h /a0 << 100.875 27.707 l -0.36631 -11.9551 Td This year’s focus is on “Beyond Supervised Learning” with four theme areas: causality, transfer learning, graph mining, and reinforcement learning. /XObject 361 0 R 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. Q 0 scn 10 0 0 10 0 0 cm Q Recent works in machine learning and deep learning have focused on learning heuristics for combinatorial optimization problems [4, 18].For the TSP, both supervised learning [23, 11] and reinforcement learning [3, 25, 15, 5, 12] methods have been proposed. q Q 1 0 0 1 504.832 514.469 Tm [ (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 /Font 476 0 R Q 1.02 0 0 1 308.862 128.821 Tm /Parent 1 0 R tions using a variety of large models show that SwapAdvisor can train models up to 12 times the GPU memory limit while achieving 53-99% of the throughput of a hypothetical baseline with infinite GPU memory. stream 1.02 0 0 1 499.557 514.469 Tm q /Type /Page q [ (The) -343.991 (proposed) -344.019 (approach) -343.983 (has) -343.998 (tw) 10.0089 (o) -344.997 (main) -344.017 (adv) 25.015 (antages\072) -501.992 (\0501\051) ] TJ [ (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 q ET /MediaBox [ 0 0 612 792 ] 0 scn 1 0 0 1 420.799 382.963 Tm ET BT q Learning Heuristics over Large Graphs via Deep Reinforcement Learning Sahil Manchanda , A. Mittal , A. Dhawan , Sourav Medya , Sayan Ranu , A. Singh Computer Science, Mathematics 96.422 5.812 m 0.985 0 0 1 50.1121 466.649 Tm 5 0 obj 0 scn Akash Mittal 15 0 obj /XObject 44 0 R ICLR 2017. /Rotate 0 /R12 9.9626 Tf 9.68329 0 Td q [ (and) -249.993 (minimum) -250.015 (v) 14.9828 (erte) 15.0122 (x) -249.993 (co) 15.0171 (v) 14.9828 (er) 55 (\056) ] TJ BT [ (straints) -245.992 (on) -246.998 (the) -245.985 (form) -245.99 (of) -246.991 (the) -245.985 (CRF) -247.015 (terms) -246.009 (to) -246 (f) 10.0101 (acilitate) -247.015 (ef) 24.9891 (fecti) 24.9987 (v) 14.9886 (e) ] TJ q 109.984 9.465 l [ (limited) -251.005 (to) -252.009 (unary) 55.9909 (\054) -251.987 (pairwis) 0.98738 (e) -251.982 (and) -251 (hand\055cr) 14.9894 (afted) -251.016 (forms) -252.014 (of) -250.984 (higher) ] TJ << 1.02 0 0 1 50.1121 176.641 Tm q Q >> [ (tasks) -208.995 (ef) 17.9961 <026369656e746c79> -209.988 (without) -208.989 (imposing) -208.984 (any) -209.985 (constr) 15.9812 (aints) -209.981 (on) -209.001 (the) -210.014 (form) ] TJ 1 0 0 1 308.862 214.049 Tm 1 0 0 1 395.813 382.963 Tm [ (to) -246 (solv) 14.9959 (e) -245.988 (the) -245.018 (problem) -246.014 (on) -244.987 (a) -245.99 (gi) 24.9842 (v) 13.9832 (en) -244.994 (dataset) -246.009 (unco) 15.0176 (v) 14.9886 (ers) -245.995 (strate) 14.9886 (gies) ] TJ [ (that) -252.994 (is) -253.997 (consistent) -253.017 (with) -254.016 (visual) -253.02 (featur) 37.0086 (es) -252.993 (of) -254.016 (the) -252.981 (ima) 10.0138 (g) 9.98639 (e) 15.0094 (\056) -314.014 (Howe) 15.0045 (ver) 112.985 (\054) ] TJ [ (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 We will use a graph embedding network, called structure2vec (S2V) [9], to represent the policy in the greedy algorithm. Q [ (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 Q 0 1 0 scn /Rotate 0 /ExtGState 472 0 R (\054) Tj /R9 cs 1.004 0 0 1 50.1121 454.694 Tm (\054) Tj /R12 9.9626 Tf >> 10 0 0 10 0 0 cm /R21 cs /Type /Page (6) Tj 0.994 0 0 1 50.1121 92.9551 Tm T* /Rotate 0 Q /Parent 1 0 R 10 0 0 10 0 0 cm 1 0 0 1 308.862 347.097 Tm 2. (\054) Tj /R12 27 0 R /R16 8.9664 Tf 11.9551 TL 11.9551 TL /Length 19934 210.248 -17.9332 Td 1 0 0 1 527.093 214.049 Tm Title:Coloring Big Graphs with AlphaGoZero. /R9 cs [ (on) -248.992 (a) -248.018 (v) 24.9988 (ariety) -248.982 (of) -249.002 (c) 0.98365 (ombinatorial) -249.016 (tasks) -249.021 (from) -248 (the) -249.006 (tra) 20.0195 (v) 15.0012 (eling) -249.021 (sales\055) ] TJ /R12 9.9626 Tf /R9 cs /Font 301 0 R /Contents 337 0 R T* 1.004 0 0 1 308.862 371.007 Tm /Author (Safa Messaoud\054 Maghav Kumar\054 Alexander G\056 Schwing) 0 scn Many recent papers have aimed to do just this — Wulfmeier et al. /Resources << 1.008 0 0 1 308.862 152.731 Tm 1 0 0 1 507.91 226.004 Tm BT (58) Tj Q q 10 0 0 10 0 0 cm 0 scn Jihun Oh, Kyunghyun Cho and Joan Bruna; Dismantle Large Networks through Deep Reinforcement Learning. /Parent 1 0 R 82.0715 0 Td 0 1 0 scn >> [ (de) 24.9818 (v) 13.9857 (eloped) -247 (\133) ] TJ [ (\056\054) -343.997 (policies\054) -342.996 (for) -323.985 (solving) -323.997 (infer) 35.9826 (ence) -324.004 (in) ] TJ 0.98 0 0 1 50.1121 371.007 Tm Sayan Ranu /Type /Page BT 10 0 0 10 0 0 cm /ExtGState 134 0 R 71.715 5.789 67.215 10.68 67.215 16.707 c In addition, the impact of budget-constraint, which is necessary for many practical scenarios, remains to be studied. Algorithm representation. 10 0 0 10 0 0 cm Q /R9 40 0 R 3 0 obj /MediaBox [ 0 0 612 792 ] /MediaBox [ 0 0 612 792 ] 10 0 0 10 0 0 cm 1.015 0 0 1 62.0672 212.507 Tm Dynamic Partial Removal: a Neural Network Heuristic for Large Neighborhood Search on Combinatorial Optimization Problems, by applying deep learning (hierarchical recurrent graph convolutional network) and reinforcement learning (PPO) - water-mirror/DPR 0 1 0 scn In this paper, we propose a framework called GCOMB to bridge these gaps. 1.001 0 0 1 50.1121 359.052 Tm Q ET 1.012 0 0 1 308.613 261.869 Tm /Type /Page /R12 9.9626 Tf 1 0 0 1 0 0 cm [ (messaou2\054) -600.005 (mkumar10\054) -600.005 (aschwing) ] TJ [18] Ian Osband, John Aslanides & … 1 0 0 1 50.1121 224.462 Tm Authors:Jiayi Huang, Mostofa Patwary, Gregory Diamos Abstract: We show that recent innovations in deep reinforcement learning can effectively color very large graphs -- a well-known NP-hard problem with clear commercial applications. >> 10 0 0 10 0 0 cm (18) Tj /I true The idea is that a machine learning method could potentially learn better heuristics by extracting useful information directly from … >> [ (through) -252.01 (lar) 18.0053 (ge) -251.014 (amounts) -252.018 (of) -251.983 (sample) -252.005 (problems\056) -313.014 (T) 79.9831 (o) -251.981 (achie) 24.988 (v) 15.0036 (e) -251.016 (this\054) ] TJ >> 1.017 0 0 1 308.503 430.783 Tm [17] Ian Osband, et al. [ (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 For example, urban infrastructure networks may enable certain racial groups to more easily access resources such as high-quality schools, grocery stores, and polling places. 0 1 0 scn “Deep Exploration via Bootstrapped DQN”. /ExtGState 129 0 R >> endstream We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. /R21 cs /Contents 399 0 R endobj /R9 cs q endobj 1 0 0 1 479.338 514.469 Tm [ (\135) -247 (and) -247.014 (a) ] TJ 67.215 22.738 71.715 27.625 77.262 27.625 c [ (rial) -249.012 (algorithm\056) -314.005 (F) 14.9917 (or) -249.019 (instance\054) -248.992 (semantic) -249.017 (image) -248.017 (se) 13.9923 (gmentation) ] TJ /Font 135 0 R 73.895 23.332 71.164 20.363 71.164 16.707 c • [ (which) -247.011 (are) -246.009 (close) -247.004 (to) -245.987 (optimal) -247.014 (b) 20.0046 (ut) -246.99 (hard) -246.994 (to) -245.987 <026e64> -247.004 (manually) 63.9847 (\054) -246.994 (since) ] TJ -10.5379 -13.9477 Td /R21 cs >> 11.9551 TL /R10 11.9552 Tf q task. BT << /R12 9.9626 Tf 10 0 0 10 0 0 cm /R12 9.9626 Tf 0 scn T* Q /ColorSpace 474 0 R /ColorSpace 482 0 R /R12 9.9626 Tf 12 0 obj 77.262 5.789 m /MediaBox [ 0 0 612 792 ] 91.531 15.016 l Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network Learning Dynamic Belief Graphs to Generalize on Text-Based Games Strongly Incremental Constituency Parsing with Graph … BT /ColorSpace << 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,[email protected] frunzhong.wang,[email protected] q [ (pr) 44.0046 (o) 10.0011 (gr) 14.9821 (am) -323.993 (heuristics\054) ] TJ [ (accurate) -285.006 (deep) -284.994 (net) -284.015 (models\054) -294.991 (challenges) -285.015 (such) -284.985 (as) -285 (inconsistent) ] TJ “Learning to Perform Physics Experiments via Deep Reinforcement Learning”. Ambuj Singh, There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. [ (puter) -357.985 (vision\056) -641.998 (F) 103.01 (or) -357.005 (instance) 9.98608 (\054) -385.995 (in) -357.989 (applications) -357.997 (lik) 10.0065 (e) -358.019 (semantic) ] TJ 1 0 0 1 515.088 514.469 Tm We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. 0 1 0 scn /ProcSet [ /PDF /Text ] /Rotate 0 Q /ca 1 0.994 0 0 1 308.862 249.914 Tm (\054) Tj /ColorSpace 133 0 R Q [ (bounding) -269.998 (box) -268.986 (detection\054) -275.996 (se) 14.9893 (gmentation) -268.986 (or) -270.007 (image) -269.003 <636c617373690263612d> ] TJ [ (CRFs) -247.99 (for) -247.01 (semantic) -248.008 (se) 16.0087 (gmentation\056) -313.983 (W) 82 (e) -248.003 (hence) -248.003 (w) 10.9926 (onder) -247.988 (whether) ] TJ Our results establish that GCOMB is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms. 0 scn /R12 9.9626 Tf q 1 0 0 1 405.815 382.963 Tm To further facilitate the combinatorial nature of the problem, GCOMB utilizes a Q-learning framework, which is made efficient through importance sampling. /ColorSpace 478 0 R 1 0 0 1 55.9461 675.067 Tm In the simulation part, the proposed method is compared with the optimal power flow method. /ProcSet [ /PDF /Text ] [ (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 /R10 14.3462 Tf 0.98 0 0 1 50.1121 490.559 Tm ET 1.014 0 0 1 308.862 382.963 Tm To further facilitate the combinatorial nature of the problem, GCOMB utilizes a Q-learning framework, which is made efficient through importance sampling. /a1 gs (f) Tj 10 0 0 10 0 0 cm /Annots [ ] [ (Process) -250.992 (\050MDP\051\056) -251.993 (T) 80.9851 (o) -252.016 (solv) 14.9927 (e) -251.002 (the) -252 (MDP) 111.979 (\054) -251.017 (we) -252.016 (assess) -250.987 (tw) 10 (o) -252.016 (reinforce\055) ] TJ BT [ (se) 39.0145 (gmentation\054) -311.016 (human) -298.988 (pose) -298.017 (estimation) -298.999 (and) -298.009 (action) -298.994 (r) 37.0012 (eco) 9.98968 (gni\055) ] TJ /ExtGState 483 0 R 0.98 0 0 1 308.862 359.052 Tm 1 0 0 -1 0 792 cm 0.984 0 0 1 308.503 285.78 Tm BT endobj endobj endobj [ (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 0 scn >> ET BT >> (27) Tj /R12 9.9626 Tf /Parent 1 0 R -11.721 -11.9551 Td 1.014 0 0 1 415.778 382.963 Tm ET >> [ (Unlik) 9.98248 (e) -258.997 (traditional) -260.013 (approaches\054) -263.004 (it) -259.011 (does) -259.001 (not) -258.997 (impose) -259.996 (an) 15.011 (y) -259.006 (con\055) ] TJ Sungyong Seo and Yan Liu; Advancing GraphSAGE with A Data-driven Node Sampling. /MediaBox [ 0 0 612 792 ] [ (pr) 44.0046 (oximation) -265.993 (methods) -266.016 (ar) 36.009 (e) -265.993 (computationally) -266 (demanding) -266.017 (and) ] TJ 1.02 0 0 1 308.862 478.604 Tm Jointly trained with the graph-aware decoder using deep reinforcement learning, our approach can effectively find optimized solutions for unseen graphs. /R9 cs /R12 11.9552 Tf endobj /ProcSet [ /PDF /Text ] 10 0 0 10 0 0 cm De Cao and Kipf [13] similarly to [11] focus on small molecular graph genera-tion, and furthermore, they do not consider the generation process as a sequence of actions. 48.406 3.066 515.188 33.723 re << A Deep Learning Framework for Graph Partitioning. 10 0 0 10 0 0 cm /ExtGState 397 0 R T* >> /R12 9.9626 Tf Q 0 1 0 scn /R12 9.9626 Tf 1 0 0 1 49.5039 347.097 Tm /XObject << [ (mantic) -349.997 (patterns\056) -619.005 (It) -350.009 (is) -350.016 (therefore) -350.009 (concei) 24.0012 (v) 24.991 (able) -351.004 (that) -350.018 (learning) ] TJ /Contents 15 0 R In addition, the impact of budget-constraint, which is necessary for many practical scenarios, remains to be studied. [ (in) -293.984 (semantic) -293.992 (se) 14.9893 (gmentation) -294.011 (problems\077) -449.992 (T) 78.9853 (o) -293.987 (study) -293.987 (this) -294.001 (we) -293.002 (de\055) ] TJ endobj [ (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 /Type /Page 8 0 obj [ (man) -247.02 (problem) -246.995 (and) -247.995 (the) -246.983 (knapsack) -247.008 (formulation) -246.998 (to) -246.998 (maximum) -248.003 (cut) ] TJ For many practical scenarios, remains to be studied approach can effectively find optimized solutions for unseen graphs marginally in. Fully observed networks jointly trained with the graph-aware decoder using deep Reinforcement learning sociotechnical networks the combinatorial of! “ learning to perform Physics experiments via deep Reinforcement learning ” and access state-of-the-art solutions, modelling a generalizeable with... 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