Mathias Lechner

Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars.

2021 · arXiv (Cornell University)

Despite the rich theoretical foundation of model-based deep reinforcement learning (RL) agents, their effectiveness in real-world robotics-applications is less studied and understood. In this paper, we, therefore, investigate how such agents generalize to real-world autonomous-vehicle …

Quantization-Aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks

2023 · Proceedings of the AAAI Conference on Artificial Intelligence

We study the problem of training and certifying adversarially robust quantized neural networks (QNNs). Quantization is a technique for making neural networks more efficient by running them using low-bit integer arithmetic and is therefore commonly …