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Found 7 papers, 0 papers with code
Intent Demonstration in General-Sum Dynamic Games via Iterative Linear-Quadratic Approximations
Autonomous agents should be able to coordinate with other agents without knowing their intents ahead of time.
Scenario-Game ADMM: A Parallelized Scenario-Based Solver for Stochastic Noncooperative Games
To accommodate this, we decompose the approximated game into a set of smaller games with few constraints for each sampled scenario, and propose a decentralized, consensus-based ADMM algorithm to efficiently compute a generalized Nash equilibrium (GNE) of the approximated game.
Motion Matters: A Novel Motion Modeling For Cross-View Gait Feature Learning
Specifically, we first extract the motion features from the encoded motion sequences in the shallow layer.
Forget Less, Count Better: A Domain-Incremental Self-Distillation Learning Benchmark for Lifelong Crowd Counting
Crowd counting has important applications in public safety and pandemic control.
Infinite-Horizon Reach-Avoid Zero-Sum Games via Deep Reinforcement Learning
We address this problem by designing a new value function with a contracting Bellman backup, where the super-zero level set, i. e., the set of states where the value function is evaluated to be non-negative, recovers the reach-avoid set.
Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification
We extend the analysis to the SDP, where the feasible set geometry is exploited to design a branching scheme that minimizes the worst-case SDP relaxation error.
Tightened Convex Relaxations for Neural Network Robustness Certification
In this paper, we consider the problem of certifying the robustness of neural networks to perturbed and adversarial input data.
