{"list":"other","total":58,"rows":[{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1806.07366","title":"Neural Ordinary Differential Equations","created":"2018-06-19","cited_by_count":3779},{"id":"1909.01377","title":"Deep Equilibrium Models","created":"2019-09-03","cited_by_count":615},{"id":"1703.00443","title":"OptNet: Differentiable Optimization as a Layer in Neural Networks","created":"2017-03-01","cited_by_count":599},{"id":"1711.00851","title":"Provable defenses against adversarial examples via the convex outer adversarial polytope","created":"2017-11-02","cited_by_count":567},{"id":"2009.00236","title":"A Survey of Deep Active Learning","created":"2020-08-30","cited_by_count":554},{"id":"1910.12430","title":"Differentiable Convex Optimization Layers","created":"2019-10-28","cited_by_count":465},{"id":"1710.08005","title":"Smart \"Predict, then Optimize\"","created":"2017-10-22","cited_by_count":339},{"id":"1810.00069","title":"Adversarial Attacks and Defences: A Survey","created":"2018-09-28","cited_by_count":259},{"id":"1809.05504","title":"Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization","created":"2018-09-14","cited_by_count":227},{"id":"1905.08539","title":"Universal Approximation with Deep Narrow Networks","created":"2019-05-21","cited_by_count":200},{"id":"1606.01885","title":"Learning to Optimize","created":"2016-06-06","cited_by_count":195},{"id":"2104.12225","title":"DC3: A learning method for optimization with hard constraints","created":"2021-04-25","cited_by_count":184},{"id":"1905.12149","title":"SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver","created":"2019-05-29","cited_by_count":173},{"id":"2103.16378","title":"End-to-End Constrained Optimization Learning: A Survey","created":"2021-03-30","cited_by_count":157},{"id":"1803.06567","title":"A Dual Approach to Scalable Verification of Deep Networks","created":"2018-03-17","cited_by_count":125},{"id":"1710.11278","title":"Approximating Continuous Functions by ReLU Nets of Minimal Width","created":"2017-10-31","cited_by_count":122},{"id":"2009.04131","title":"SoK: Certified Robustness for Deep Neural Networks","created":"2020-09-09","cited_by_count":105},{"id":"1708.02691","title":"Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations","created":"2017-08-09","cited_by_count":104},{"id":"1705.09412","title":"Learning to Optimize: Training Deep Neural Networks for Wireless Resource Management","created":"2017-05-26","cited_by_count":96},{"id":"1909.10461","title":"Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methods","created":"2019-09-19","cited_by_count":94},{"id":"2002.05999","title":"Adversarial Distributional Training for Robust Deep Learning","created":"2020-02-14","cited_by_count":79},{"id":"1803.10150","title":"Learning to Branch","created":"2018-03-27","cited_by_count":78},{"id":"1909.04866","title":"Deep Declarative Networks: A New Hope","created":"2019-09-11","cited_by_count":78},{"id":"1910.01213","title":"Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow","created":"2019-09-27","cited_by_count":77},{"id":"1908.02788","title":"The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms","created":"2019-08-07","cited_by_count":65},{"id":"1806.10644","title":"Efficient representation and approximation of model predictive control laws via deep learning","created":"2018-06-27","cited_by_count":58},{"id":"2110.02672","title":"Physics-Informed Neural Networks for AC Optimal Power Flow","created":"2021-10-06","cited_by_count":54},{"id":"1910.14448","title":"DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow","created":"2019-10-30","cited_by_count":50},{"id":"2007.01002","title":"DeepOPF: A Feasibility-Optimized Deep Neural Network Approach for AC Optimal Power Flow Problems","created":"2020-07-02","cited_by_count":47},{"id":"2104.04413","title":"Provable Repair of Deep Neural Networks","created":"2021-04-09","cited_by_count":42},{"id":"1802.09639","title":"Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets","created":"2018-02-26","cited_by_count":36},{"id":"1905.04479","title":"DeepOPF: Deep Neural Network for DC Optimal Power Flow","created":"2019-05-11","cited_by_count":32},{"id":"1905.08860","title":"Learning Warm-Start Points for AC Optimal Power Flow","created":"2019-05-21","cited_by_count":32},{"id":"1902.05607","title":"Learning for DC-OPF: Classifying active sets using neural nets","created":"2019-02-14","cited_by_count":31},{"id":"2006.11029","title":"Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks","created":"2020-06-19","cited_by_count":28},{"id":"1910.08842","title":"Machine Learning for AC Optimal Power Flow","created":"2019-10-19","cited_by_count":24},{"id":"2107.00465","title":"Physics-Informed Neural Networks for Minimising Worst-Case Violations in DC Optimal Power Flow","created":"2021-06-28","cited_by_count":23},{"id":"1805.02777","title":"What game are we playing? End-to-end learning in normal and extensive form games","created":"2018-05-07","cited_by_count":22},{"id":"2106.02601","title":"Learning Hard Optimization Problems: A Data Generation Perspective","created":"2021-06-04","cited_by_count":21},{"id":"1803.01785","title":"Differentiable Submodular Maximization","created":"2018-03-05","cited_by_count":19},{"id":"2008.11827","title":"Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation","created":"2020-08-26","cited_by_count":19},{"id":"2108.04214","title":"Neural Network Repair with Reachability Analysis","created":"2021-08-09","cited_by_count":19},{"id":"2006.00544","title":"Data-driven Optimal Power Flow: A Physics-Informed Machine Learning Approach","created":"2020-05-31","cited_by_count":18},{"id":"2103.11793","title":"DeepOPF-V: Solving AC-OPF Problems Efficiently","created":"2021-03-22","cited_by_count":18},{"id":"2009.09109","title":"A Convex Neural Network Solver for DCOPF with Generalization Guarantees","created":"2020-09-18","cited_by_count":17},{"id":"1912.00319","title":"Solutions of DC OPF are Never AC Feasible","created":"2019-12-01","cited_by_count":16},{"id":"1902.09592","title":"Verification of Non-Linear Specifications for Neural Networks","created":"2019-02-25","cited_by_count":14},{"id":"2006.16356","title":"High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow","created":"2020-06-29","cited_by_count":13},{"id":"1801.07809","title":"Statistical Learning For DC Optimal Power Flow","created":"2018-01-23","cited_by_count":12},{"id":"1907.04694","title":"Data-Driven Screening of Network Constraints for Unit Commitment","created":"2019-07-10","cited_by_count":11},{"id":"2009.03147","title":"DeepOPF+: A Deep Neural Network Approach for DC Optimal Power Flow for Ensuring Feasibility","created":"2020-09-07","cited_by_count":11},{"id":"2002.04091","title":"Learning to Solve Network Flow Problems via Neural Decoding","created":"2020-02-10","cited_by_count":9},{"id":"1806.06790","title":"Towards Distributed Energy Services: Decentralizing Optimal Power Flow with Machine Learning","created":"2018-06-14","cited_by_count":8},{"id":"1808.01009","title":"Data-driven Local Control Design using Optimization and Machine Learning Techniques","created":"2018-08-02","cited_by_count":5},{"id":"1904.01757","title":"Implied Constraint Satisfaction in Power System Optimization: The Impacts of Load Variations","created":"2019-04-03","cited_by_count":5},{"id":"1809.04153","title":"Joint Chance Constraints in AC Optimal Power Flow: Improving Bounds through Learning","created":"2018-09-11","cited_by_count":3},{"id":"1911.03019","title":"Learning-Accelerated ADMM for Distributed Optimal Power Flow","created":"2019-11-08","cited_by_count":1}],"next_cursor":null}