25 citations · 56 across the 16 of their papers we have counts for
6 papers · 1 filter
Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks
Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee +2
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 runn…
Learning Control Policies for Stochastic Systems with Reach-avoid Guarantees
Đorđe Žikelić, Mathias Lechner, Thomas A. Henzinger +1
We study the problem of learning controllers for discrete-time non-linear stochastic dynamical systems with formal reach-avoid guarantees. This work presents the first method for p…
PyHopper -- Hyperparameter optimization
Mathias Lechner, Ramin Hasani, Philipp Neubauer +2
Hyperparameter tuning is a fundamental aspect of machine learning research. Setting up the infrastructure for systematic optimization of hyperparameters can take a significant amou…
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap
Mathias Lechner, Ramin Hasani, Alexander Amini +3
There is an ever-growing zoo of modern neural network models that can efficiently learn end-to-end control from visual observations. These advanced deep models, ranging from convol…
Liquid Structural State-Space Models
Ramin Hasani, Mathias Lechner, Tsun-Hsuan Wang +3
A proper parametrization of state transition matrices of linear state-space models (SSMs) followed by standard nonlinearities enables them to efficiently learn representations from…
Learning Stabilizing Policies in Stochastic Control Systems
Đorđe Žikelić, Mathias Lechner, Krishnendu Chatterjee +1
In this work, we address the problem of learning provably stable neural network policies for stochastic control systems. While recent work has demonstrated the feasibility of certi…