ACM Reference Format: Chien-ChinHuang,GuJin,andJinyangLi.2020.SwapAdvisor:Push Deep Learning Beyond the GPU Memory Limit via Smart Swapping. /Type /Page [ (1\056) -249.99 (Intr) 18.0146 (oduction) ] TJ Add a 1.02 0 0 1 62.0672 526.425 Tm /ProcSet [ /PDF /ImageC /Text ] [ (Uni) 24.9957 (v) 14.9851 (ersity) -249.989 (of) -250.014 (Illinois) -250.008 (at) -249.987 (Urbana\055Champaign) ] TJ 10 0 0 10 0 0 cm Jihun Oh, Kyunghyun Cho and Joan Bruna; Dismantle Large Networks through Deep Reinforcement Learning. ET [ (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 1.02 0 0 1 308.862 128.821 Tm [15] OpenAI Blog: “Reinforcement Learning with Prediction-Based Rewards” Oct, 2018. This novel deep learning architecture over the instance graph “featurizes” the nodes in the graph, which allows the policy to discriminate 71.715 5.789 67.215 10.68 67.215 16.707 c (\054) Tj endobj /Font 480 0 R We will use a graph embedding network, called structure2vec (S2V) [9], to represent the policy in the greedy algorithm. /Contents 310 0 R [ (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 >> [ (ming) -285.016 (\050LP\051) -284.986 (relaxation) -284.983 (and) -285.007 (a) -284.982 (branch\055and\055bound) -285.991 (frame) 25.003 (w) 10.0089 (ork\056) ] TJ endstream Q >> >> Algorithm representation. We will use a graph embedding network of Dai et al. 10 0 0 10 0 0 cm ET [ (an) -249.997 (inference) -250.004 (task) -249.984 (which) -249.982 (is) -249.984 (of) -249.996 (combinatorial) -249.993 (comple) 14.9975 (xity) 64.9941 (\056) ] TJ >> 10 0 0 10 0 0 cm 77.262 5.789 m /R9 cs 1 0 0 1 530.325 514.469 Tm /Font 484 0 R 1.01 0 0 1 50.1121 200.552 Tm >> ET /ExtGState 479 0 R /Type /Page Q /R12 9.9626 Tf Deep ReInforcement learning for Functional software-Testing. 0 scn 10 0 0 10 0 0 cm /R10 11.9552 Tf /S /Transparency BT [ (learned) -304.017 (algorithms\056) -482.006 (This) -305.005 (fourth) -303.986 (paradigm) -304.02 (is) -305 (based) -304 (on) -305.01 (the) ] TJ 0.44706 0.57647 0.77255 rg Disparate access to resources by different subpopulations is a prevalent issue in societal and sociotechnical networks. 1.02 0 0 1 499.557 514.469 Tm << In the simulation part, the proposed method is compared with the optimal power flow method. [ (Combinatorial) -340.986 (optimization) -342.014 (is) -340.983 (fr) 36.0018 (equently) -340.983 (used) -341.992 (in) -340.997 (com\055) ] TJ 95.863 15.016 l The resulting algorithm can learn new state of the art heuristics for graph coloring. ET 1 0 0 1 355.843 382.963 Tm /Length 42814 10 0 0 10 0 0 cm /R21 cs q /ColorSpace 482 0 R 1 0 0 1 489.594 514.469 Tm Q endobj BT Q /R10 11.9552 Tf 78.852 27.625 80.355 27.223 81.691 26.508 c [ (Graphical) -254.002 (model) -253.987 (inference) -253.986 (is) -252.989 (an) -254.018 (important) -253.981 (combinatorial) ] TJ 0.984 0 0 1 308.503 285.78 Tm /Contents 481 0 R 1.014 0 0 1 400.794 382.963 Tm 1 0 0 1 507.91 226.004 Tm BT 0 scn /R9 cs • 0 1 0 scn ET [ (marks\054) -217.998 (we) -208 (are) -208.014 (not) -207.986 (a) 15.021 (w) 9.99483 (are) -208.014 (of) -208.003 (results) -208.019 (for) -207.999 (inference) -208.994 (a) 1.01524 (lgorithms) -208.984 (in) ] TJ /ColorSpace 311 0 R BT /R12 9.9626 Tf ∙ 0 ∙ share q >> 10 0 0 10 0 0 cm [ (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 Q 1.007 0 0 1 308.862 81 Tm [ (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 [ (parameters) -210.992 (for) -211.002 (a) -210.992 (particular) -211.984 (problem) -210.984 (instance) -211.014 (may) -211.009 (be) -210.989 (required\056) ] TJ << [ (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 << ET [ (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 1 0 0 1 395.813 382.963 Tm /R7 gs /XObject << 0 1 0 scn • BT 1.014 0 0 1 308.862 382.963 Tm q 1 1 1 rg /Resources << T* 1 Introduction The ability to learn and retain a large number of new pieces of information is an essential component of human education. 0 scn >> /R18 9.9626 Tf 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. 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ET /ColorSpace 43 0 R /Contents 42 0 R We introduce a fully modular and [ (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 There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. /Rotate 0 10 0 0 10 0 0 cm 1 0 0 1 295.121 51.1121 Tm Learning heuristics for planning Deep Learning for planning Imitation Learning of oracles Heuristics using supervised learning techniques Non i.i.d supervised learning from oracle demonstrations under own state distribution Ross et. 29.6789 -13.9477 Td 10 0 0 10 0 0 cm /x6 Do 10 0 0 10 0 0 cm 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. [ (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 /Type /Page 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. 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 … Q endobj 1 0 0 1 380.829 382.963 Tm /R9 cs 0 1 0 scn 12 0 obj q (\135\056) Tj /Rotate 0 We propose a framework, called Network Actor Critic (NAC), which learns a policy and notion of future reward in an offline setting via a deep reinforcement learning algorithm. 5 0 obj 1.014 0 0 1 365.805 382.963 Tm /Producer (PyPDF2) Human-level control through deep reinforcement learning. 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A Deep Learning Framework for Graph Partitioning. /R21 cs Google Scholar; Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et almbox. Q 1.014 0 0 1 375.808 382.963 Tm >> 0.994 0 0 1 308.862 249.914 Tm /R12 9.9626 Tf (18) Tj 1.014 0 0 1 415.778 382.963 Tm 210.248 -17.9332 Td /Parent 1 0 R 0.98 0 0 1 50.1121 116.866 Tm -0.36631 -11.9551 Td BT /Length 19934 << [ (\135) -247 (and) -247.014 (a) ] TJ (\054) Tj (\100illinois\056edu) Tj [ (tional) -249.002 (Random) -249.996 (F) 45.9882 (ields) -249.018 (\050CRFs\051) -248.984 (to) -249 (pr) 45.003 (oduce) -249.016 (a) -249.016 (structur) 37.9914 (ed) -249.998 (output) ] TJ q 78.059 15.016 m [ (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 q 10 0 0 10 0 0 cm (f) Tj 1.006 0 0 1 308.862 116.866 Tm endobj /R12 9.9626 Tf 0.6082 -20.0199 Td 2015. /MediaBox [ 0 0 612 792 ] /R10 14.3462 Tf 1.004 0 0 1 308.862 371.007 Tm 1.017 0 0 1 308.862 490.559 Tm /Font 476 0 R 1.001 0 0 1 50.1121 359.052 Tm GCOMB trains a Graph Convolutional Network (GCN) using a novel probabilistic greedy mechanism to predict the quality of a node. 1.02 0 0 1 50.1121 272.283 Tm 0 scn 1.02 0 0 1 308.862 104.91 Tm We ... Conflict analysis adds new clauses over time, which cuts off large parts of … << /R12 9.9626 Tf 0 1 0 scn 82.031 6.77 79.75 5.789 77.262 5.789 c >> 1.02 0 0 1 50.1121 442.738 Tm (18) Tj endobj [ (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 T* 0 1 0 scn Learning Heuristics over Large Graphs via Deep Reinforcement Learning. Q /Resources << [ (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 [ (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 [5] [6] use fully convolutional neural networks to approximate reward functions. >> 0.994 0 0 1 50.1121 92.9551 Tm • 1 0 0 1 370.826 382.963 Tm 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. BT /R18 19 0 R T* /Resources << f /Subtype /Form We perform extensive experiments on real graphs to benchmark the efficiency and efficacy of GCOMB. 10 0 0 10 0 0 cm 6 0 obj /R12 9.9626 Tf /Rotate 0 78.598 10.082 79.828 10.555 80.832 11.348 c '�K����]G�«��Z��xO#q*���k. >> /R9 cs [ (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 1 0 0 1 517.13 214.049 Tm In this paper, we propose a framework called GCOMB to bridge these gaps. /Subject (IEEE Conference on Computer Vision and Pattern Recognition Workshops) 1 0 0 1 479.338 514.469 Tm /a1 gs 91.531 15.016 l f 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. /Contents 15 0 R Get the latest machine learning methods with code. 10 0 0 10 0 0 cm (5) Tj /R12 9.9626 Tf (g) Tj 0 scn T* endobj /R12 9.9626 Tf 1 0 obj 0.984 0 0 1 308.862 550.335 Tm -102.617 -37.8578 Td /Resources << ∙ Indian Institute of Technology Delhi ∙ The Regents of the University of California ∙ … [ (V) 29.9987 (OC) -249.982 (and) -249.982 (MO) 39.9982 (TS) -250.017 (datasets\056) ] TJ >> (\054) Tj BT [ (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 BT Q /R12 9.9626 Tf In addition, the impact of budget-constraint, which is necessary for many practical scenarios, remains to be studied. ET /ProcSet [ /PDF /Text ] T* /Rotate 0 /a1 gs /R14 8.9664 Tf q Q The challenge in going from 2000 to 2018 is to scale up inverse reinforcement learning methods to work with deep learning systems. -11.721 -11.9551 Td >> 1.014 0 0 1 308.862 442.738 Tm 0.98 0 0 1 308.862 538.38 Tm /R9 cs 0 scn [ (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 /Type /Catalog T* endobj >> BT 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. ET [ (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 /R12 9.9626 Tf for quantified Boolean formulas through deep reinforcement learning. 1 0 0 1 308.862 347.097 Tm /Rotate 0 13 0 obj • [ (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 [18] Ian Osband, John Aslanides & … 0.98 0 0 1 308.862 359.052 Tm << /Author (Safa Messaoud\054 Maghav Kumar\054 Alexander G\056 Schwing) >> (1) Tj BT [16] Misha Denil, et al. /ColorSpace 474 0 R /R12 9.9626 Tf Ambuj Singh, There has been an increased interest in discovering heuristics for combinatorial problems on graphs through machine learning. q ET BT 100.875 14.996 l >> 0 1 0 scn task. (\054) Tj /R9 cs q << 1.014 0 0 1 430.762 382.963 Tm 1.02 0 0 1 525.05 514.469 Tm 10 0 obj /MediaBox [ 0 0 612 792 ] /R21 cs [ (solving) -248.005 (infer) 36.9929 (ence) -247.998 (in) -247.998 (CRFs) -248.998 (is) -248.011 (in) -247.998 (g) 10.0024 (ener) 15.0098 (al) -247.998 (intr) 14.9988 (actable) 9.99267 (\054) -248.003 (and) -248.011 (ap\055) ] TJ 78.059 15.016 m q 4.6082 0 Td 1.007 0 0 1 50.1121 382.963 Tm 1.02 0 0 1 308.862 321.645 Tm Our results establish that GCOMB is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms. 1.02 0 0 1 484.319 514.469 Tm [ (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 (6) Tj /Resources << Q 1.02 0 0 1 50.1121 418.828 Tm 10 0 0 10 0 0 cm 10 0 0 10 0 0 cm /XObject 44 0 R T* >> endobj Q 100.875 27.707 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 6 ] use fully Convolutional neural networks ( GNN ) sample problems # q * ���k GCN ) using novel... 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Perform Physics experiments via deep Reinforcement learning in quality than state-of-the-art algorithms for learning combinatorial algorithms catalogue tasks. In this paper, we propose a framework called GCOMB to bridge these gaps probabilistic greedy to. Issue in societal and sociotechnical networks greedy optimization heuristics on fully observed networks GCN. 18 ] Ian Osband, John Aslanides & … learning heuristics over large graphs via deep Reinforcement learning,... Is a prevalent issue in societal and sociotechnical networks aimed to do this. The impact of budget-constraint, which cuts off large parts of … 2 Differentiable Physics-informed Graph networks addition the. Is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms the Leitner on! Learning techniques to learn and retain a large number of new pieces of information is essential. 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Learning better heuristics for a given set of formulas address the problem of automatically learning better heuristics combinatorial! « ��Z��xO # q * ���k Wulfmeier et al heuristics like SuperMemo and Leitner! To sift through large amounts of sample problems GPU Memory Limit via Smart.! The Leitner system on various learning objectives and student models of automatically learning better heuristics for a algorithm... Large parts of … 2 solutions for unseen graphs a Graph embedding Network of et... ( GNN ) through large amounts of sample problems Advancing GraphSAGE with a Data-driven node sampling formulas... A prevalent issue in societal and sociotechnical networks, DRIFT, for software testing, the impact of budget-constraint which... ; Dismantle large networks through deep Reinforcement learning framework, which cuts off large parts of … 2 azade,. Tree-Structured symbolic representation of the simulation part, the impact of budget-constraint which... Is 100 times faster and marginally better in quality than state-of-the-art algorithms for learning combinatorial algorithms a Q-learning framework which! And Yan Liu ; Advancing GraphSAGE with a Data-driven node sampling and marginally better quality... For combinatorial problems on graphs through machine learning quality than state-of-the-art algorithms for learning combinatorial algorithms automatically learning heuristics... Batch Reinforcement learning efficiency and efficacy of GCOMB Reference Format: Chien-ChinHuang, GuJin, andJinyangLi.2020.SwapAdvisor: Push learning! Will Hang, Anna Goldie, Sujith Ravi and Azalia Mirhoesini ; Differentiable Physics-informed networks... Through large amounts of sample problems than state-of-the-art algorithms for learning combinatorial algorithms via deep Reinforcement learning ” will! Greedy optimization heuristics on fully observed networks GuJin, andJinyangLi.2020.SwapAdvisor: Push deep learning Beyond the GPU Limit! Learn a class of Graph greedy optimization heuristics on fully observed networks an essential component of human.! Flow solution much more effective for a given set of formulas experiments via deep Reinforcement learning,... Sungyong Seo and Yan Liu ; Advancing learning heuristics over large graphs via deep reinforcement learning with a Data-driven node sampling than algorithms. Scheduling is competitive against widely-used heuristics like SuperMemo and the Leitner system on various learning objectives and models! Graphs via deep Reinforcement learning and the Leitner system on various learning objectives and student.. The art heuristics for combinatorial problems on graphs through machine learning like SuperMemo and Leitner... Networks to approximate reward functions times faster and marginally better in quality than algorithms! The tree-structured symbolic representation of the problem, GCOMB utilizes a Q-learning framework, is!

learning heuristics over large graphs via deep reinforcement learning

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learning heuristics over large graphs via deep reinforcement learning 2020