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Found 10 papers, 1 papers with code
Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI
The size of the dictionary and the patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables.
Interpreting Finite Automata for Sequential Data
Automaton models are often seen as interpretable models.
Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata
This paper focuses on detecting anomalies in a digital video broadcasting (DVB) system from providers' perspective.
Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile
Understanding multi-vehicle interactive behaviors with temporal sequential observations is crucial for autonomous vehicles to make appropriate decisions in an uncertain traffic environment.
Learning a Safety Verifiable Adaptive Cruise Controller from Human Driving Data
Imitation learning provides a way to automatically construct a controller by mimicking human behavior from data.
Multi-modal Segment Assemblage Network for Ad Video Editing with Importance-Coherence Reward
The existing method performs well at video segmentation stages but suffers from the problems of dependencies on extra cumbersome models and poor performance at the segment assemblage stage.
Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning
In this work, we propose a curriculum learning-based framework by transitioning from a simpler vehicle model to a more complex real environment to teach the reinforcement learning agent a policy closer to the optimal policy.
Robust Control Barrier Functions for Safe Control Under Uncertainty Using Extended State Observer and Output Measurement
Our approach provides a less conservative estimation error bound than other disturbance observer-based CBFs.
Towards Low-Barrier Cybersecurity Research and Education for Industrial Control Systems
On our testbed, we validate our proposed intrusion detection model called Minimal Threshold and Window SVM (MinTWin SVM) that utilizes unsupervised machine learning via a one-class SVM in combination with a sliding window and classification threshold.
Disturbance Rejection-Guarded Learning for Vibration Suppression of Two-Inertia Systems
On the other hand, machine learning benefits from an additional assurance layer provided by the ESO, as any imperfections in the machine learning model can be compensated for by the ESO.
