b'Radu Grosu'

research

∙ 10/28/2022

IB-U-Nets: Improving medical image segmentation tasks with 3D Inductive Biased kernels

Despite the success of convolutional neural networks for 3D medical-imag...

Shrajan Bhandary, et al. ∙

research

∙ 10/20/2022

Safe Policy Improvement in Constrained Markov Decision Processes

The automatic synthesis of a policy through reinforcement learning (RL) ...

Luigi Berducci, et al. ∙

research

∙ 10/20/2022

Pruning by Active Attention Manipulation

Filter pruning of a CNN is typically achieved by applying discrete masks...

Zahra Babaiee, et al. ∙

research

∙ 06/27/2022

Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data

Modern tourism in the 21st century is facing numerous challenges. One of...

Julian Lemmel, et al. ∙

research

∙ 06/02/2022

Entangled Residual Mappings

Residual mappings have been shown to perform representation learning in ...

Mathias Lechner, et al. ∙

research

∙ 04/15/2022

End-to-End Sensitivity-Based Filter Pruning

In this paper, we present a novel sensitivity-based filter pruning algor...

Zahra Babaiee, et al. ∙

research

∙ 03/31/2022

Multi-Agent Spatial Predictive Control with Application to Drone Flocking (Extended Version)

We introduce the novel concept of Spatial Predictive Control (SPC) to so...

Andreas Brandstätter, et al. ∙

research

∙ 10/29/2021

3D-OOCS: Learning Prostate Segmentation with Inductive Bias

Despite the great success of convolutional neural networks (CNN) in 3D m...

Shrajan Bhandary, et al. ∙

research

∙ 10/06/2021

From STL Rulebooks to Rewards

The automatic synthesis of neural-network controllers for autonomous age...

Edgar A. Aguilar, et al. ∙

research

∙ 09/21/2021

From English to Signal Temporal Logic

Formal methods provide very powerful tools and techniques for the design...

Jie He, et al. ∙

research

∙ 07/18/2021

GoTube: Scalable Stochastic Verification of Continuous-Depth Models

We introduce a new stochastic verification algorithm that formally quant...

Sophie A. Neubauer, et al. ∙

research

∙ 06/13/2021

On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification

Robustness to variations in lighting conditions is a key objective for a...

Zahra Babaiee, et al. ∙

research

∙ 03/15/2021

Adversarial Training is Not Ready for Robot Learning

Adversarial training is an effective method to train deep learning model...

Mathias Lechner, et al. ∙

research

∙ 03/08/2021

Model-based versus Model-free Deep Reinforcement Learning for Autonomous Racing Cars

Despite the rich theoretical foundation of model-based deep reinforcemen...

Axel Brunnbauer, et al. ∙

research

∙ 12/18/2020

A Distributed Simplex Architecture for Multi-Agent Systems

We present Distributed Simplex Architecture (DSA), a new runtime assuran...

Usama Mehmood, et al. ∙

research

∙ 12/16/2020

On The Verification of Neural ODEs with Stochastic Guarantees

We show that Neural ODEs, an emerging class of time-continuous neural ne...

Sophie A. Neubauer, et al. ∙

research

∙ 12/14/2020

Lagrangian Reachtubes: The Next Generation

We introduce LRT-NG, a set of techniques and an associated toolset that ...

Sophie A. Neubauer, et al. ∙

research

∙ 06/08/2020

Liquid Time-constant Networks

We introduce a new class of time-continuous recurrent neural network mod...

Ramin Hasani, et al. ∙

research

∙ 06/01/2020

Learning Distributed Controllers for V-Formation

We show how a high-performing, fully distributed and symmetric neural V-...

Shouvik Roy, et al. ∙

research

∙ 02/26/2020

ResNets, NeuralODEs and CT-RNNs are Particular Neural Regulatory Networks

This paper shows that ResNets, NeuralODEs, and CT-RNNs, are particular n...

Radu Grosu, et al. ∙

research

∙ 02/20/2020

V-Formation via Model Predictive Control

We present recent results that demonstrate the power of viewing the prob...

Radu Grosu, et al. ∙

research

∙ 10/11/2019

A Nonparametric Bayesian Model for Sparse Temporal Multigraphs

As the availability and importance of temporal interaction data–such as ...

Elahe Ghalebi, et al. ∙

research

∙ 08/26/2019

Neural Flocking: MPC-based Supervised Learning of Flocking Controllers

We show how a distributed flocking controller can be synthesized using d...

Shouvik Roy, et al. ∙

research

∙ 08/01/2019

Neural Simplex Architecture

We present the Neural Simplex Architecture (NSA), a new approach to runt...

Dung Phan, et al. ∙

research

∙ 05/28/2019

Dynamic Nonparametric Edge-Clustering Model for Time-Evolving Sparse Networks

Interaction graphs, such as those recording emails between individuals o...

Elahe Ghalebi, et al. ∙

research

∙ 11/01/2018

Liquid Time-constant Recurrent Neural Networks as Universal Approximators

In this paper, we introduce the notion of liquid time-constant (LTC) rec...

Ramin M. Hasani, et al. ∙

research

∙ 10/16/2018

A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems

The Internet of Things (IoT) is an ubiquitous system connecting many dif...

Denise Ratasich, et al. ∙

research

∙ 09/20/2018

Tight Continuous-Time Reachtubes for Lagrangian Reachability

We introduce continuous Lagrangian reachability (CLRT), a new algorithm ...

Jacek Cyranka, et al. ∙

research

∙ 09/11/2018

Re-purposing Compact Neuronal Circuit Policies to Govern Reinforcement Learning Tasks

We propose an effective method for creating interpretable control agents...

Ramin M. Hasani, et al. ∙

research

∙ 09/11/2018

Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks

In this paper, we introduce a novel method to interpret recurrent neural...

Ramin M. Hasani, et al. ∙

research

∙ 07/26/2018

Neural State Classification for Hybrid Systems

We introduce the State Classification Problem (SCP) for hybrid systems, ...

Dung Phan, et al. ∙

research

∙ 05/27/2018

Dynamic Network Model from Partial Observations

Can evolving networks be inferred and modeled without directly observing...

Elahe Ghalebi, et al. ∙

research

∙ 05/21/2018

Adaptive Neighborhood Resizing for Stochastic Reachability in Multi-Agent Systems

We present DAMPC, a distributed, adaptive-horizon and adaptive-neighborh...

Anna Lukina, et al. ∙

research

∙ 03/22/2018

Neuronal Circuit Policies

We propose an effective way to create interpretable control agents, by r...

Mathias Lechner, et al. ∙

research

∙ 02/11/2018

An Algebraic Framework for Runtime Verification

Runtime verification (RV) is a pragmatic and scalable, yet rigorous tech...

Stefan Jaksic, et al. ∙

research

∙ 11/09/2017

Worm-level Control through Search-based Reinforcement Learning

Through natural evolution, nervous systems of organisms formed near-opti...

Mathias Lechner, et al. ∙

research

∙ 11/04/2017

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...

Magdalena Fuchs, et al. ∙

research

∙ 10/27/2017

Declarative vs Rule-based Control for Flocking Dynamics

The popularity of rule-based flocking models, such as Reynolds' classic ...

Usama Mehmood, et al. ∙

research

∙ 05/16/2017

Lagrangian Reachabililty

We introduce LRT, a new Lagrangian-based ReachTube computation algorithm...

Jacek Cyranka, et al. ∙

research

∙ 03/18/2017

An Automated Auto-encoder Correlation-based Health-Monitoring and Prognostic Method for Machine Bearings

This paper studies an intelligent ultimate technique for health-monitori...

Ramin M. Hasani, et al. ∙

research

∙ 03/18/2017

SIM-CE: An Advanced Simulink Platform for Studying the Brain of Caenorhabditis elegans

We introduce SIM-CE, an advanced, user-friendly modeling and simulation ...

Ramin M. Hasani, et al. ∙

research

∙ 03/18/2017

Non-Associative Learning Representation in the Nervous System of the Nematode Caenorhabditis elegans

Caenorhabditis elegans (C. elegans) illustrated remarkable behavioral pl...

Ramin M. Hasani, et al. ∙

research

∙ 12/21/2016

ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans

We introduce ARES, an efficient approximation algorithm for generating o...

Anna Lukina, et al. ∙

research

∙ 02/13/2015

Deep Neural Programs for Adaptive Control in Cyber-Physical Systems

We introduce Deep Neural Programs (DNP), a novel programming paradigm fo...

Konstantin Selyunin, et al. ∙

Radu Grosu

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