Search Results for author:
Found 6 papers, 2 papers with code
Parallelization Techniques for Verifying Neural Networks
Inspired by recent successes with parallel optimization techniques for solving Boolean satisfiability, we investigate a set of strategies and heuristics that aim to leverage parallel computing to improve the scalability of neural network verification.
Visual Depth Mapping from Monocular Images using Recurrent Convolutional Neural Networks
A reliable sense-and-avoid system is critical to enabling safe autonomous operation of unmanned aircraft.
Decomposition Methods with Deep Corrections for Reinforcement Learning
In contexts where an agent interacts with multiple entities, utility decomposition can be used to separate the global objective into local tasks considering each individual entity independently.
Toward Scalable Verification for Safety-Critical Deep Networks
The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability.
Towards Proving the Adversarial Robustness of Deep Neural Networks
Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations.
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems.
