Search Results for author: Scott A. Smolka

Found 10 papers, 5 papers with code

SpecNFS: A Challenge Dataset Towards Extracting Formal Models from Natural Language Specifications

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.

Dependency Parsing Domain Adaptation +7

A Barrier Certificate-based Simplex Architecture for Systems with Approximate and Hybrid Dynamics

no code implementations20 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.

On The Verification of Neural ODEs with Stochastic Guarantees

no code implementations16 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.

Lagrangian Reachtubes: The Next Generation

1 code implementation14 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.

MPC-guided Imitation Learning of Neural Network Policies for the Artificial Pancreas

1 code implementation3 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.

Bayesian Inference Imitation Learning +1

Neural Simplex Architecture

no code implementations1 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.

continuous-control Continuous Control +1

Neural State Classification for Hybrid Systems

1 code implementation26 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.

Classification General Classification

How to Learn a Model Checker

no code implementations5 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.

BIG-bench Machine Learning

A Component-Based Simplex Architecture for High-Assurance Cyber-Physical Systems

1 code implementation16 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

ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans

no code implementations21 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.

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