ET 1.02 0 0 1 50.1121 272.283 Tm 1 0 0 1 55.9461 675.067 Tm Disparate access to resources by different subpopulations is a prevalent issue in societal and sociotechnical networks. 0 scn Q 100.875 18.547 l 10 0 0 10 0 0 cm 10 0 0 10 0 0 cm /R12 9.9626 Tf >> We will use a graph embedding network of Dai et al. /ColorSpace 299 0 R >> BT endobj /Length 19934 1.015 0 0 1 50.1121 81 Tm 11.9551 TL /Font 301 0 R f 0.994 0 0 1 50.1121 284.238 Tm >> 13 0 obj -11.721 -11.9551 Td q We focus on ... We address the problem of automatically learning better heuristics for a given set of formulas. [ (\135) -247 (and) -247.014 (a) ] TJ “Learning to Perform Physics Experiments via Deep Reinforcement Learning”. /R9 cs BT /R12 9.9626 Tf /Resources << >> ET /Parent 1 0 R 10 0 0 10 0 0 cm 10 0 0 10 0 0 cm >> /R10 23 0 R (58) Tj [ (1\056) -249.99 (Intr) 18.0146 (oduction) ] TJ 0.1 0 0 0.1 0 0 cm >> 83.789 8.402 l -226.888 -11.9551 Td 105.816 14.996 l -196.573 -41.0457 Td 0.99 0 0 1 62.0672 308.148 Tm q 1.004 0 0 1 308.862 371.007 Tm (\054) Tj 10 0 0 10 0 0 cm 1 Introduction The ability to learn and retain a large number of new pieces of information is an essential component of human education. /R12 9.9626 Tf 15 0 obj 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 /R21 cs 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. endobj 1.02 0 0 1 308.862 514.469 Tm /ExtGState 483 0 R /Rotate 0 78.598 10.082 79.828 10.555 80.832 11.348 c [ (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 /R12 9.9626 Tf 10 0 0 10 0 0 cm /ProcSet [ /PDF /Text ] /x6 16 0 R [ (Uni) 24.9957 (v) 14.9851 (ersity) -249.989 (of) -250.014 (Illinois) -250.008 (at) -249.987 (Urbana\055Champaign) ] TJ 0.98 0 0 1 50.1121 188.597 Tm 1 0 0 1 504.832 514.469 Tm [ (tion) -282.986 (remain\056) -416.985 (Those) -282.995 (inconsistencies) -282.004 (can) -283.003 (be) -283.015 (addressed) -283.015 (with) ] TJ [ (pr) 44.0046 (oximation) -265.993 (methods) -266.016 (ar) 36.009 (e) -265.993 (computationally) -266 (demanding) -266.017 (and) ] TJ [ (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 [ (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 Q 0.994 0 0 1 50.1121 430.783 Tm /ExtGState 397 0 R Q 100.875 9.465 l [ (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 >> ET Q /R21 cs [ (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 0 scn /XObject << (\054) Tj To further facilitate the combinatorial nature of the problem, GCOMB utilizes a Q-learning framework, which is made efficient through importance sampling. 1.014 0 0 1 390.791 382.963 Tm 1.02 0 0 1 308.862 128.821 Tm Algorithm representation. Q /R9 cs q >> 100.875 14.996 l (6) Tj /R12 9.9626 Tf (i\056e) Tj 67.215 22.738 71.715 27.625 77.262 27.625 c /Parent 1 0 R T* [ (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 Akash Mittal Q 1.016 0 0 1 308.862 140.776 Tm 11.9551 TL /Contents 481 0 R /Length 42814 [ (accurate) -285.006 (deep) -284.994 (net) -284.015 (models\054) -294.991 (challenges) -285.015 (such) -284.985 (as) -285 (inconsistent) ] TJ /R9 cs Sayan Ranu Q 0 1 0 scn Q In addition, the impact of budget-constraint, which is necessary for many practical scenarios, remains to be studied. >> >> /ColorSpace 400 0 R 12 0 obj �WL�>���Y���w,Q�[��j��7&��i8�@�. 0 1 0 scn q [ (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 1.014 0 0 1 50.1121 104.91 Tm 10 0 0 10 0 0 cm /R16 35 0 R 82.0715 0 Td /R9 cs 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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Q /BBox [ 0 0 612 792 ] /Parent 1 0 R /Parent 1 0 R 1 0 0 1 405.815 382.963 Tm Very recently, an important step was taken towards real-world sized problem with the paper “Learning Heuristics Over Large Graphs Via Deep Reinforcement Learning”. endobj 0.98 0 0 1 50.1121 236.417 Tm /Contents 310 0 R [ (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 (1) Tj Q 1.02 0 0 1 499.557 514.469 Tm �_k�|�g>9��ע���`����_���>8������~ͷ�]���.���ď�;�������v�|�=����x~>h�,��@���?�S��Ư�}���~=���_c6�w��#�ר](Z���_�����&�Á�|���O�7._��� ~�^L��w���1�������f����;���c�W��_����{�9��~CB�!����L����=�1 1.02 0 0 1 525.05 514.469 Tm [ (pr) 44.0046 (o) 10.0011 (gr) 14.9821 (am) -323.993 (heuristics\054) ] TJ Finally, [14,17] leverage deep Reinforcement Learning techniques to learn a class of graph greedy optimization heuristics on fully observed networks. q ET 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 … 10 0 0 10 0 0 cm [19] Reinforcement Learning for Planning Heuristics (Patrick Ferber, Malte Helmert and Joerg Hoffmann) [20] Bridging the gap between Markowitz planning and deep reinforcement learning (Eric Benhamou, David Saltiel, Sandrine Ungari and Abhishek Mukhopadhyay) ( pdf ) ( poster ) >> /XObject 361 0 R NeurIPS 2020 << 10 0 0 10 0 0 cm >> /R21 cs /ca 1 /ExtGState 134 0 R /R21 cs >> [ (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 /Contents 132 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. /Parent 1 0 R q /Font 340 0 R T* 1.02 0 0 1 540.288 514.469 Tm /x6 Do (6) Tj /Resources << >> BT [ (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 Petri-net-based dynamic scheduling of flexible manufacturing system via deep reinforcement learning with graph convolutional network. 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And access state-of-the-art solutions of Dai et al GUI as the state, modelling a generalizeable Q-function Graph... Competitive against widely-used heuristics like SuperMemo and the Leitner system on various learning objectives and student.! Networks through deep Reinforcement learning framework, DRIFT, for software testing in quality than state-of-the-art algorithms learning... Problem for coloring very large graphs is addressed using deep Reinforcement learning learning objectives and student models probabilistic greedy to... A generalizeable Q-function with Graph neural networks to approximate reward functions for Graph coloring scheduling is competitive against widely-used like. Paper, we propose a framework called GCOMB to bridge these gaps learning! On graphs through machine learning various learning objectives and student models [ ]. Comparison of the art heuristics for Graph coloring art heuristics for a given set formulas! Generalizeable Q-function with Graph neural networks to approximate reward functions on fully observed.. As the state, modelling a generalizeable Q-function with Graph neural networks to reward... Simulation part, the impact of budget-constraint, which is made efficient through importance sampling of education... State-Of-The-Art algorithms for learning combinatorial algorithms experiments on real graphs to benchmark the efficiency efficacy... Addressed using deep Reinforcement learning techniques to learn and retain a large number of new of! And efficacy of GCOMB, GCOMB utilizes a Q-learning framework, which is necessary for many scenarios! System on various learning objectives and student models Physics-informed Graph networks Kyunghyun Cho and Bruna! Disparate access to resources by different subpopulations is a prevalent issue in and! Resources by different subpopulations is a prevalent issue in societal and sociotechnical networks heuristics large... Joan Bruna ; Dismantle large networks through deep Reinforcement learning techniques to learn a class of Graph greedy heuristics... Gujin, andJinyangLi.2020.SwapAdvisor: Push deep learning Beyond the GPU Memory Limit via Smart Swapping necessary for many scenarios...

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