25 citations · 57 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
Interactive Analysis of CNN Robustness
Stefan Sietzen, Mathias Lechner, Judy Borowski +2
While convolutional neural networks (CNNs) have found wide adoption as state-of-the-art models for image-related tasks, their predictions are often highly sensitive to small input…
On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification
Zahra Babaiee, Ramin Hasani, Mathias Lechner +2
Robustness to variations in lighting conditions is a key objective for any deep vision system. To this end, our paper extends the receptive field of convolutional neural networks w…
Causal Navigation by Continuous-time Neural Networks
Charles Vorbach, Ramin Hasani, Alexander Amini +2
Imitation learning enables high-fidelity, vision-based learning of policies within rich, photorealistic environments. However, such techniques often rely on traditional discrete-ti…
Adversarial Training is Not Ready for Robot Learning
Mathias Lechner, Ramin Hasani, Radu Grosu +2
Adversarial training is an effective method to train deep learning models that are resilient to norm-bounded perturbations, with the cost of nominal performance drop. While adversa…