{"list":"other","total":53,"rows":[{"id":"1702.01135","title":"Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks","created":"2017-02-03","cited_by_count":744},{"id":"1703.00443","title":"OptNet: Differentiable Optimization as a Layer in Neural Networks","created":"2017-03-01","cited_by_count":599},{"id":"1910.12430","title":"Differentiable Convex Optimization Layers","created":"2019-10-28","cited_by_count":465},{"id":"1907.08194","title":"Neural Probabilistic Logic Programming in DeepProbLog","created":"2019-07-18","cited_by_count":394},{"id":"1903.11199","title":"Control Barrier Functions: Theory and Applications","created":"2019-03-27","cited_by_count":372},{"id":"1709.02540","title":"The Expressive Power of Neural Networks: A View from the Width","created":"2017-09-08","cited_by_count":320},{"id":"1711.11157","title":"A Semantic Loss Function for Deep Learning with Symbolic Knowledge","created":"2017-11-29","cited_by_count":270},{"id":"1705.01320","title":"Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks","created":"2017-05-03","cited_by_count":242},{"id":"1903.06758","title":"Algorithms for Verifying Deep Neural Networks","created":"2019-03-15","cited_by_count":221},{"id":"2112.05124","title":"Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation","created":"2021-12-09","cited_by_count":209},{"id":"1905.08539","title":"Universal Approximation with Deep Narrow Networks","created":"2019-05-21","cited_by_count":200},{"id":"2012.13635","title":"Logic Tensor Networks","created":"2020-12-25","cited_by_count":199},{"id":"2104.12225","title":"DC3: A learning method for optimization with hard constraints","created":"2021-04-25","cited_by_count":184},{"id":"1805.08328","title":"Verifiable Reinforcement Learning via Policy Extraction","created":"2018-05-22","cited_by_count":177},{"id":"1710.11278","title":"Approximating Continuous Functions by ReLU Nets of Minimal Width","created":"2017-10-31","cited_by_count":122},{"id":"2001.06116","title":"Learning Stable Deep Dynamics Models","created":"2020-01-17","cited_by_count":111},{"id":"1705.08302","title":"Anatomically Constrained Neural Networks (ACNN): Application to Cardiac Image Enhancement and Segmentation","created":"2017-05-22","cited_by_count":109},{"id":"2206.00426","title":"Semantic Probabilistic Layers for Neuro-Symbolic Learning","created":"2022-06-01","cited_by_count":103},{"id":"1903.01287","title":"Safety Verification and Robustness Analysis of Neural Networks via Quadratic Constraints and Semidefinite Programming","created":"2019-03-04","cited_by_count":101},{"id":"1506.03648","title":"Constrained Convolutional Neural Networks for Weakly Supervised Segmentation","created":"2015-06-11","cited_by_count":100},{"id":"2006.08859","title":"Minimum Width for Universal Approximation","created":"2020-06-16","cited_by_count":94},{"id":"1910.14574","title":"An Abstraction-Based Framework for Neural Network Verification","created":"2019-10-31","cited_by_count":68},{"id":"2010.10151","title":"Coherent Hierarchical Multi-Label Classification Networks","created":"2020-10-20","cited_by_count":65},{"id":"2109.10317","title":"Introduction to Neural Network Verification","created":"2021-09-21","cited_by_count":63},{"id":"2206.08321","title":"Equivariant Descriptor Fields: SE(3)-Equivariant Energy-Based Models for End-to-End Visual Robotic Manipulation Learning","created":"2022-06-16","cited_by_count":56},{"id":"2212.12393","title":"A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference","created":"2022-12-23","cited_by_count":50},{"id":"2009.12612","title":"Neurosymbolic Reinforcement Learning with Formally Verified Exploration","created":"2020-09-26","cited_by_count":49},{"id":"2006.05487","title":"Probably Approximately Correct Constrained Learning","created":"2020-06-09","cited_by_count":47},{"id":"2202.09400","title":"Equivariant Transporter Network","created":"2022-02-18","cited_by_count":46},{"id":"1706.02025","title":"Imposing Hard Constraints on Deep Networks: Promises and Limitations","created":"2017-06-07","cited_by_count":43},{"id":"2103.05134","title":"Constrained Learning with Non-Convex Losses","created":"2021-03-08","cited_by_count":42},{"id":"2104.04413","title":"Provable Repair of Deep Neural Networks","created":"2021-04-09","cited_by_count":42},{"id":"2011.08105","title":"Enforcing robust control guarantees within neural network policies","created":"2020-11-16","cited_by_count":40},{"id":"2304.11726","title":"End-to-End Feasible Optimization Proxies for Large-Scale Economic Dispatch","created":"2023-04-23","cited_by_count":35},{"id":"2205.14268","title":"NeuPSL: Neural Probabilistic Soft Logic","created":"2022-05-27","cited_by_count":34},{"id":"2110.03303","title":"Universal Approximation Under Constraints is Possible with Transformers","created":"2021-10-07","cited_by_count":32},{"id":"2307.08336","title":"RAYEN: Imposition of Hard Convex Constraints on Neural Networks","created":"2023-07-17","cited_by_count":30},{"id":"2402.07251","title":"Physics-Informed Neural Networks with Hard Linear Equality Constraints","created":"2024-02-11","cited_by_count":30},{"id":"2402.04823","title":"How Realistic Is Your Synthetic Data? Constraining Deep Generative Models for Tabular Data","created":"2024-02-07","cited_by_count":29},{"id":"2011.08421","title":"Reachability-based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control","created":"2020-11-17","cited_by_count":28},{"id":"1906.10417","title":"Probabilistic model predictive safety certification for learning-based control","created":"2019-06-25","cited_by_count":22},{"id":"2009.07330","title":"Training neural networks under physical constraints using a stochastic augmented Lagrangian approach","created":"2020-09-15","cited_by_count":20},{"id":"2211.01340","title":"POLICE: Provably Optimal Linear Constraint Enforcement for Deep Neural Networks","created":"2022-11-02","cited_by_count":19},{"id":"2407.13917","title":"LinSATNet: The Positive Linear Satisfiability Neural Networks","created":"2024-07-18","cited_by_count":16},{"id":"2306.02426","title":"Resilient Constrained Learning","created":"2023-06-04","cited_by_count":15},{"id":"1902.09592","title":"Verification of Non-Linear Specifications for Neural Networks","created":"2019-02-25","cited_by_count":14},{"id":"2203.03034","title":"A Unified View of SDP-based Neural Network Verification through Completely Positive Programming","created":"2022-03-06","cited_by_count":12},{"id":"2307.10459","title":"A New Computationally Simple Approach for Implementing Neural Networks with Output Hard Constraints","created":"2023-07-19","cited_by_count":12},{"id":"2303.03157","title":"Data-Driven Control with Inherent Lyapunov Stability","created":"2023-03-06","cited_by_count":6},{"id":"2410.00976","title":"Learning Dissipative Chaotic Dynamics with Boundedness Guarantees","created":"2024-10-01","cited_by_count":5},{"id":"2111.10785","title":"Differentiable Projection for Constrained Deep Learning","created":"2021-11-21","cited_by_count":4},{"id":"2402.11362","title":"Exploiting T-norms for Deep Learning in Autonomous Driving","created":"2024-02-17","cited_by_count":4},{"id":"2403.07043","title":"A Collision Cone Approach for Control Barrier Functions","created":"2024-03-11","cited_by_count":3}],"next_cursor":null}