/Resources << Q 73.895 23.332 71.164 20.363 71.164 16.707 c /R16 8.9664 Tf [ (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 Learning Heuristics over Large Graphs via Deep Reinforcement Learning. /R12 9.9626 Tf /Parent 1 0 R endobj /Subject (IEEE Conference on Computer Vision and Pattern Recognition Workshops) 0.999 0 0 1 308.862 394.918 Tm >> 1.012 0 0 1 308.613 261.869 Tm /Parent 1 0 R Q [ (comple) 15.0079 (xity) -246.996 (is) -247.983 (linear) -247.001 (in) -247.011 (arbitrary) -246.986 (potential) -247.98 (orders) -247.006 (while) -247.006 (clas\055) ] TJ q /ProcSet [ /PDF /Text ] 0 scn q >> [ (Exact) -199.017 (algorithms) -199.004 (are) -199.011 (often) -199.005 (based) -199.018 (on) -199 (solving) -199.014 (an) -198.986 (Inte) 15 (ger) -198.984 (Linear) ] TJ Learning heuristics over large graphs via deep reinforcement learning. /Parent 1 0 R /MediaBox [ 0 0 612 792 ] �WL�>���Y���w,Q�[��j��7&��i8�@�. 1 0 0 1 50.1121 224.462 Tm 105.816 14.996 l << 1.008 0 0 1 308.862 152.731 Tm /MediaBox [ 0 0 612 792 ] /Resources << (\054) Tj 0 scn 10 0 0 10 0 0 cm 1.014 0 0 1 308.862 382.963 Tm f (1) Tj The comparison of the simulation results shows that the proposed method has better performance than the optimal power flow solution. BT In this paper the authors trained a Graph Convolutional Network to solve large instances of problems such as Minimum Vertex Cover (MVC) and Maximum Coverage Problem (MCP). 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Conﬂict analysis adds new clauses over time, which cuts off large parts of … 10 0 0 10 0 0 cm This paper presents an open-source, parallel AI environment (named OpenGraphGym) to facilitate the application of reinforcement learning (RL) algorithms to address combinatorial graph optimization problems.This environment incorporates a basic deep reinforcement learning method, and several graph embeddings to capture graph features, it also allows users to … (6) Tj 1 0 0 1 49.5039 347.097 Tm Browse our catalogue of tasks and access state-of-the-art solutions. /R21 cs q /ExtGState 475 0 R (\054) Tj 5 0 obj Q /R21 cs 1.017 0 0 1 308.503 430.783 Tm q 0.98 0 0 1 50.1121 236.417 Tm Q >> /ExtGState 129 0 R [ (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 1.016 0 0 1 308.862 140.776 Tm /Type /Page (93) Tj [ (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 q • Ambuj Singh, There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. 100.875 18.547 l 10 0 0 10 0 0 cm /R12 9.9626 Tf q 0 scn /Font 55 0 R -0.36631 -11.9551 Td We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. 1.02 0 0 1 62.0672 526.425 Tm /Resources << /Font << >> 71.715 5.789 67.215 10.68 67.215 16.707 c [ (Uni) 24.9957 (v) 14.9851 (ersity) -249.989 (of) -250.014 (Illinois) -250.008 (at) -249.987 (Urbana\055Champaign) ] TJ >> 87.273 24.305 l Learning Heuristics over Large Graphs via Deep Reinforcement Learning Akash Mittal 1, Anuj Dhawan , Sourav Medya2, Sayan Ranu1, Ambuj Singh2 1Indian Institute of Technology Delhi 2University of California, Santa Barbara 1 fcs1150208, Anuj.Dhawan.cs115, sayanranu g@cse.iitd.ac.in , 2 medya, ambuj @cs.ucsb.edu Abstract In this paper, we propose a deep reinforcement /R14 8.9664 Tf Q 4 0 obj T* (2016), called struc-ture2vec (S2V), to represent the policy in the greedy algorithm. (g) Tj ICLR 2017. Q /S /Transparency /R9 cs Anuj Dhawan Q Sayan Ranu Algorithm representation. << q h /R12 9.9626 Tf (i\056e) Tj 1.02 0 0 1 308.862 478.604 Tm 0 1 0 scn (5) Tj /R12 9.9626 Tf 82.031 6.77 79.75 5.789 77.262 5.789 c [ (1\056) -249.99 (Intr) 18.0146 (oduction) ] TJ Q /R21 cs Our experiments show that the proposed model outperforms both METIS, a state-of-the-art graph partitioning algorithm, and an LSTM-based encoder-decoder model, in about 70% of the test cases. 3 Problem De nition /R12 9.9626 Tf ET 0 1 0 scn 1.02 0 0 1 525.05 514.469 Tm Learning Heuristics over Large Graphs via Deep Reinforcement Learning Sahil Manchanda , A. Mittal , A. Dhawan , Sourav Medya , Sayan Ranu , A. Singh Computer Science, Mathematics Learning Trajectories for Visual-Inertial System Calibration via Model-based Heuristic Deep Reinforcement Learning Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion Learning a Decision Module by Imitating Driver’s Control Behaviors /Font 301 0 R 10 0 0 10 0 0 cm Q 10 0 0 10 0 0 cm /Resources << Title:Coloring Big Graphs with AlphaGoZero. /Resources << 15 0 obj [ (we) -254.018 (can) -254.003 (learn) -254.013 (heuristics) -253.995 (to) -253.99 (address) -254.003 (graphical) -253.988 (model) -254.003 (inference) ] TJ /Annots [ ] /MediaBox [ 0 0 612 792 ] [ (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 [ (While) -224.982 (the) -224.017 (aforementioned) -224.997 (learning) -225.017 (based) -223.982 (techniques) -225.007 (ha) 20.9849 (v) 15.0085 (e) ] TJ ET BT q BT /Type /Page 0.994 0 0 1 308.862 249.914 Tm 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. BT 1.02 0 0 1 308.862 514.469 Tm /XObject 403 0 R /R21 cs We will use a graph embedding network of Dai et al. /Rotate 0 /R12 9.9626 Tf /Filter /FlateDecode 10 0 0 10 0 0 cm 0 scn 1.014 0 0 1 415.778 382.963 Tm [ (P) 14.9905 (articularly) -291.995 (for) -291.004 (lar) 16.9954 (ge) -291.011 (problems\054) -303.987 (repeated) -291.01 (solving) -291.983 (of) -290.996 (linear) ] TJ /Type /Page /R12 9.9626 Tf /R21 cs /R12 9.9626 Tf In AAAI . [ (come) -245.983 (in) -246.019 (three) -246.014 (paradigms\072) -306.013 (e) 14.0192 (xact\054) -246.016 (approximate) -246.018 (and) -245.991 (heuristic\056) ] TJ [ (deep) -249.995 (net) -249.99 (guided) -250.015 (Monte) -250.012 (Carlo) -250.017 (T) 35.0187 (ree) -250.007 (Search) -249.993 (\050MCTS\051) -250.002 (\133) ] TJ [ (using) -246.017 (r) 37.0135 (einfor) 35.9841 (cement) -246.015 (learning) 14.9894 (\056) -306.988 (Our) -246.003 (method) -245.996 (solves) -246.985 (infer) 36.98 (ence) ] TJ “Deep Exploration via Bootstrapped DQN”. 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BT /Rotate 0 >> [ (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 BT 1.02 0 0 1 308.862 321.645 Tm Deep ReInforcement learning for Functional software-Testing. 1 0 0 1 0 0 cm /a1 gs << 10 0 0 10 0 0 cm BT 10 0 obj 29.6789 -13.9477 Td 1 1 1 rg Learning heuristics over large graphs via deep reinforcement learning. 1 0 0 1 395.813 382.963 Tm 0.98 0 0 1 50.1121 371.007 Tm >> ET This year’s focus is on “Beyond Supervised Learning” with four theme areas: causality, transfer learning, graph mining, and reinforcement learning. q Disparate access to resources by different subpopulations is a prevalent issue in societal and sociotechnical networks. 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. /R18 9.9626 Tf q 1 0 0 1 55.9461 675.067 Tm /ProcSet [ /PDF /Text ] /R10 11.9552 Tf 96.449 27.707 l %PDF-1.3 Many recent papers have aimed to do just this — Wulfmeier et al. 10 0 0 10 0 0 cm 0 1 0 scn 1 0 0 1 308.862 347.097 Tm /ExtGState 472 0 R 0.994 0 0 1 50.1121 284.238 Tm 1.02 0 0 1 50.1121 176.641 Tm 1 0 0 1 308.862 214.049 Tm ET ET 1.014 0 0 1 430.762 382.963 Tm /R12 9.9626 Tf /Contents 399 0 R Our results establish that GCOMB is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms. [18] Ian Osband, John Aslanides & … endobj >> endobj 1.02 0 0 1 50.1121 272.283 Tm /R9 cs Q /R12 9.9626 Tf /XObject 44 0 R 1 0 0 1 515.088 514.469 Tm /Type /XObject 1 0 0 -1 0 792 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. 0 scn 1.02 0 0 1 50.1121 442.738 Tm /CA 0.5 0 scn /R12 9.9626 Tf /R18 19 0 R << /I true 0 scn [ (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 10 0 0 10 0 0 cm BT [ (been) -265.005 (sho) 23.9844 (wn) -264.988 (to) -266 (perform) -265 (e) 15.0061 (xtremely) -265.008 (well) -266.017 (on) -264.993 (classical) -264.984 (bench\055) ] TJ /ExtGState 314 0 R >> /CA 1 /R12 9.9626 Tf /ProcSet [ /PDF /Text ] 0 scn 1.007 0 0 1 517.872 226.004 Tm [ (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 (18) Tj 8 0 obj T* /Contents 310 0 R 1.01 0 0 1 50.1121 200.552 Tm 71.164 13.051 73.895 10.082 77.262 10.082 c << >> endobj /ColorSpace 133 0 R [ (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 /a0 gs /R21 38 0 R 100.875 9.465 l >> BT This novel deep learning architecture over the instance graph “featurizes” the nodes in the graph, capturing the properties of a node in the context of its graph … Q ET 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. 2 0 obj /R12 9.9626 Tf 10 0 0 10 0 0 cm -91.7548 -11.9551 Td ET << /R12 9.9626 Tf ET Q endobj /Parent 1 0 R 1 0 0 1 380.829 382.963 Tm 0.98 0 0 1 308.862 359.052 Tm q [ (is) -341.982 (more) -340.987 (ef) 23.9916 <02> 1 (cient) -342.008 (than) -341.016 (traditional) -342.004 (approaches) -340.985 (as) -342.004 (inference) ] TJ 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). /Font 317 0 R BT /Type /Page ET /ProcSet [ /PDF /Text ] /Kids [ 3 0 R 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R 13 0 R ] 10 0 0 10 0 0 cm 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. Our results establish that GCOMB is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms. 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ET [ (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 0 1 0 scn [ (V) 29.9987 (OC) -249.982 (and) -249.982 (MO) 39.9982 (TS) -250.017 (datasets\056) ] TJ The deep reinforcement learning approach is applied to solve the optimal control problem. /R16 35 0 R • f endstream 03/08/2019 ∙ by Akash Mittal, et al. /Rotate 0 2015. q << << /R21 cs ET /R10 11.9552 Tf (\054) Tj /BBox [ 0 0 612 792 ] Sahil Manchanda T* >> << [ (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 1 0 0 1 530.325 514.469 Tm BT 0.98 0 0 1 50.1121 116.866 Tm /R12 9.9626 Tf 109.984 5.812 l q Q (93) Tj T* [ (hibiti) 24.997 (v) 13.9989 (e\056) -549.007 (Approximation) -326.988 (algorithms) -326.999 (address) -326.013 (this) -326.983 (concern\054) ] TJ /Type /Page 16 0 obj [ (A) -229.981 (fourth) -230.984 (paradigm) -230.014 (has) -231.004 (been) -230.014 (considered) -229.984 (since) -231.014 (the) -230.019 (early) -229.999 (2000s) ] TJ /Parent 1 0 R ET A Deep Learning Framework for Graph Partitioning. 0.994 0 0 1 50.1121 92.9551 Tm 1.02 0 0 1 320.817 200.552 Tm BT /Producer (PyPDF2) [ (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 [ (ho) 26.0129 (we) 25.014 (v) 15.0066 (er) 40.9883 (\054) -250.997 (often) -251.017 (at) -249.987 (the) -250.984 (e) 15.98 (xpense) -250.986 (of) -250.012 (weak) -250.991 (optimality) -250.018 (guarantees\056) ] TJ /R12 9.9626 Tf ACM Reference Format: Chien-ChinHuang,GuJin,andJinyangLi.2020.SwapAdvisor:Push Deep Learning Beyond the GPU Memory Limit via Smart Swapping. /ProcSet [ /PDF /Text ] [ (and) -269.017 (g) 5.00445 (ained) -269.003 (popularity) -269.008 (ag) 5.01646 (ain) -268.986 (recently) -269.995 (\133) ] TJ 10 0 0 10 0 0 cm 1.014 0 0 1 308.862 176.641 Tm (85) Tj /Contents 337 0 R /R21 cs /Parent 1 0 R /Contents 132 0 R -226.888 -11.9551 Td 9.68329 0 Td /R21 cs endobj >> /Pages 1 0 R Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi and Azalia Mirhoesini; Differentiable Physics-informed Graph Networks. 1 0 0 1 405.815 382.963 Tm << [ (accurate) -285.006 (deep) -284.994 (net) -284.015 (models\054) -294.991 (challenges) -285.015 (such) -284.985 (as) -285 (inconsistent) ] TJ /Font 476 0 R [ (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 [15] OpenAI Blog: “Reinforcement Learning with Prediction-Based Rewards” Oct, 2018. /R12 9.9626 Tf endobj NeurIPS 2020 [ (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 /Contents 298 0 R q ET >> Q [ (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 0 1 0 scn 1.014 0 0 1 375.808 382.963 Tm /Parent 1 0 R 95.863 15.016 l [ (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 [ (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 105.816 18.547 l /Rotate 0 /R9 cs /Resources << 100.875 27.707 l 0.6082 -20.0199 Td << 82.684 15.016 l ET h >> T* We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. it is much more effective for a learning algorithm to sift through large amounts of sample problems. 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