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A Distributed Simplex Architecture for Multi-Agent Systems
We present Distributed Simplex Architecture (DSA), a new runtime assuran...
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On The Verification of Neural ODEs with Stochastic Guarantees
We show that Neural ODEs, an emerging class of time-continuous neural ne...
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Lagrangian Reachtubes: The Next Generation
We introduce LRT-NG, a set of techniques and an associated toolset that ...
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Liquid Time-constant Networks
We introduce a new class of time-continuous recurrent neural network mod...
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Learning Distributed Controllers for V-Formation
We show how a high-performing, fully distributed and symmetric neural V-...
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ResNets, NeuralODEs and CT-RNNs are Particular Neural Regulatory Networks
This paper shows that ResNets, NeuralODEs, and CT-RNNs, are particular n...
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V-Formation via Model Predictive Control
We present recent results that demonstrate the power of viewing the prob...
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A Nonparametric Bayesian Model for Sparse Temporal Multigraphs
As the availability and importance of temporal interaction data–such as ...
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Neural Flocking: MPC-based Supervised Learning of Flocking Controllers
We show how a distributed flocking controller can be synthesized using d...
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Neural Simplex Architecture
We present the Neural Simplex Architecture (NSA), a new approach to runt...
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Dynamic Nonparametric Edge-Clustering Model for Time-Evolving Sparse Networks
Interaction graphs, such as those recording emails between individuals o...
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Liquid Time-constant Recurrent Neural Networks as Universal Approximators
In this paper, we introduce the notion of liquid time-constant (LTC) rec...
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A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems
The Internet of Things (IoT) is an ubiquitous system connecting many dif...
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Tight Continuous-Time Reachtubes for Lagrangian Reachability
We introduce continuous Lagrangian reachability (CLRT), a new algorithm ...
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Re-purposing Compact Neuronal Circuit Policies to Govern Reinforcement Learning Tasks
We propose an effective method for creating interpretable control agents...
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Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks
In this paper, we introduce a novel method to interpret recurrent neural...
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Neural State Classification for Hybrid Systems
We introduce the State Classification Problem (SCP) for hybrid systems, ...
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Dynamic Network Model from Partial Observations
Can evolving networks be inferred and modeled without directly observing...
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Adaptive Neighborhood Resizing for Stochastic Reachability in Multi-Agent Systems
We present DAMPC, a distributed, adaptive-horizon and adaptive-neighborh...
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Neuronal Circuit Policies
We propose an effective way to create interpretable control agents, by r...
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An Algebraic Framework for Runtime Verification
Runtime verification (RV) is a pragmatic and scalable, yet rigorous tech...
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Worm-level Control through Search-based Reinforcement Learning
Through natural evolution, nervous systems of organisms formed near-opti...
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Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging Recording
Individual Neurons in the nervous systems exploit various dynamics. To c...
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Declarative vs Rule-based Control for Flocking Dynamics
The popularity of rule-based flocking models, such as Reynolds' classic ...
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Lagrangian Reachabililty
We introduce LRT, a new Lagrangian-based ReachTube computation algorithm...
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An Automated Auto-encoder Correlation-based Health-Monitoring and Prognostic Method for Machine Bearings
This paper studies an intelligent ultimate technique for health-monitori...
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SIM-CE: An Advanced Simulink Platform for Studying the Brain of Caenorhabditis elegans
We introduce SIM-CE, an advanced, user-friendly modeling and simulation ...
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Non-Associative Learning Representation in the Nervous System of the Nematode Caenorhabditis elegans
Caenorhabditis elegans (C. elegans) illustrated remarkable behavioral pl...
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ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans
We introduce ARES, an efficient approximation algorithm for generating o...
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Deep Neural Programs for Adaptive Control in Cyber-Physical Systems
We introduce Deep Neural Programs (DNP), a novel programming paradigm fo...
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