25 citations · 55 across the 10 of their papers we have counts for
13 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…
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…
Infinite Time Horizon Safety of Bayesian Neural Networks
Mathias Lechner, Đorđe Žikelić, Krishnendu Chatterjee +1
Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network's prediction. We consider the problem of v…