1 code implementation • LREC 2022 • Sayontan Ghosh, Amanpreet Singh, Alex Merenstein, Wei Su, Scott A. Smolka, Erez Zadok, Niranjan Balasubramanian
Evaluations show that even when using a state-of-the-art language model, there is significant room for improvement, with the best models achieving an F1 score of only 60. 5 and 33. 3 in the named-entity-recognition and dependency-link-prediction sub-tasks, respectively.
no code implementations • 20 Feb 2022 • Amol Damare, Shouvik Roy, Roshan Sharma, Keith DSouza, Scott A. Smolka, Scott D. Stoller
Furthermore, Bb-Simplex features a new automated method for deriving, from the barrier certificate, the conditions for switching between the controllers.
no code implementations • 16 Dec 2020 • Sophie Gruenbacher, Ramin Hasani, Mathias Lechner, Jacek Cyranka, Scott A. Smolka, Radu Grosu
We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by solving a set of global-optimization problems.
1 code implementation • 14 Dec 2020 • Sophie Gruenbacher, Jacek Cyranka, Mathias Lechner, Md. Ariful Islam, Scott A. Smolka, Radu Grosu
Secondly, it computes the next reachset as the intersection of two balls: one based on the Cartesian metric and the other on the new metric.
1 code implementation • 3 Mar 2020 • Hongkai Chen, Nicola Paoletti, Scott A. Smolka, Shan Lin
Even though model predictive control (MPC) is currently the main algorithm for insulin control in the artificial pancreas (AP), it usually requires complex online optimizations, which are infeasible for resource-constrained medical devices.
no code implementations • 1 Aug 2019 • Dung T. Phan, Radu Grosu, Nils Jansen, Nicola Paoletti, Scott A. Smolka, Scott D. Stoller
NSA not only provides safety assurances in the presence of a possibly unsafe neural controller, but can also improve the safety of such a controller in an online setting via retraining, without overly degrading its performance.
1 code implementation • 26 Jul 2018 • Dung Phan, Nicola Paoletti, Timothy Zhang, Radu Grosu, Scott A. Smolka, Scott D. Stoller
We introduce the State Classification Problem (SCP) for hybrid systems, and present Neural State Classification (NSC) as an efficient solution technique.
no code implementations • 5 Dec 2017 • Dung Phan, Radu Grosu, Nicola Paoletti, Scott A. Smolka, Scott D. Stoller
We show how machine-learning techniques, particularly neural networks, offer a very effective and highly efficient solution to the approximate model-checking problem for continuous and hybrid systems, a solution where the general-purpose model checker is replaced by a model-specific classifier trained by sampling model trajectories.
1 code implementation • 16 Apr 2017 • Dung Phan, Junxing Yang, Matthew Clark, Radu Grosu, John D. Schierman, Scott A. Smolka, Scott D. Stoller
We present Component-Based Simplex Architecture (CBSA), a new framework for assuring the runtime safety of component-based cyber-physical systems (CPSs).
Systems and Control
no code implementations • 21 Dec 2016 • Anna Lukina, Lukas Esterle, Christian Hirsch, Ezio Bartocci, Junxing Yang, Ashish Tiwari, Scott A. Smolka, Radu Grosu
Inspired by Importance Splitting, the length of the horizon and the number of particles are chosen such that at least one particle reaches a next-level state, that is, a state where the cost decreases by a required delta from the previous-level state.