no code implementations • ICML 2020 • Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
We propose a neural information processing system which is obtained by re-purposing the function of a biological neural circuit model to govern simulated and real-world control tasks.
no code implementations • 21 Dec 2024 • Zahra Babaiee, Peyman M. Kiasari, Daniela Rus, Radu Grosu
Motivated by recent observations of clusterable repeating patterns in depthwise separable CNNs (DS-CNNs) trained on ImageNet, we extend this investigation across various domains and datasets.
no code implementations • 11 Dec 2024 • Dominik Freinberger, Julian Lemmel, Radu Grosu, Sofiene Jerbi
In this work, we bridge this gap and present a hybrid model combining a PQC with classical feature encoding and post-processing layers that is capable of tackling Atari games.
no code implementations • 25 Oct 2024 • Mugdim Bublin, Heimo Hirner, Antoine-Martin Lanners, Radu Grosu
To illustrate the practical application of our proposed architecture, we present a case study focusing on water management in the Carinthian community of Neuhaus.
1 code implementation • 23 Oct 2024 • Axel Brunnbauer, Julian Lemmel, Zahra Babaiee, Sophie Neubauer, Radu Grosu
Reinforcement learning algorithms for mean-field games offer a scalable framework for optimizing policies in large populations of interacting agents.
no code implementations • 15 Jul 2024 • Shrajan Bhandary, Dejan Kuhn, Zahra Babaiee, Tobias Fechter, Simon K. B. Spohn, Constantinos Zamboglou, Anca-Ligia Grosu, Radu Grosu
Accurate segmentation of prostate tumours from PET images presents a formidable challenge in medical image analysis.
no code implementations • 26 Jun 2024 • Elpiniki Maria Lygizou, Michael Reiter, Margarita Maurer-Granofszky, Michael Dworzak, Radu Grosu
Acute Leukemia is the most common hematologic malignancy in children and adolescents.
1 code implementation • 26 Mar 2024 • Axel Brunnbauer, Luigi Berducci, Peter Priller, Dejan Nickovic, Radu Grosu
Especially in real-world application domains, such as autonomous driving, auto-curriculum generation is considered vital for obtaining robust and general policies.
no code implementations • 30 Jan 2024 • Mónika Farsang, Sophie A. Neubauer, Radu Grosu
We introduce liquid-resistance liquid-capacitance neural networks (LRCs), a neural-ODE model which considerably improve the generalization, accuracy, and biological plausibility of electrical equivalent circuits (EECs), liquid time-constant networks (LTCs), and saturated liquid time-constant networks (STCs), respectively.
no code implementations • 25 Jan 2024 • Zahra Babaiee, Peyman M. Kiasari, Daniela Rus, Radu Grosu
Recent advances in depthwise-separable convolutional neural networks (DS-CNNs) have led to novel architectures, that surpass the performance of classical CNNs, by a considerable scalability and accuracy margin.
no code implementations • 18 Jan 2024 • Zahra Babaiee, Peyman M. Kiasari, Daniela Rus, Radu Grosu
In this study, we present evidence suggesting that depthwise convolutional kernels are effectively replicating the structural intricacies of the biological receptive fields observed in the mammalian retina.
no code implementations • 21 Nov 2023 • Mónika Farsang, Mathias Lechner, David Lung, Ramin Hasani, Daniela Rus, Radu Grosu
In this work we aim to determine the impact of using chemical synapses compared to electrical synapses, in both sparse and all-to-all connected networks.
no code implementations • 8 Nov 2023 • Julian Lemmel, Radu Grosu
In this paper we propose real-time recurrent reinforcement learning (RTRRL), a biologically plausible approach to solving discrete and continuous control tasks in partially-observable markov decision processes (POMDPs).
1 code implementation • 19 Sep 2023 • Luigi Berducci, Shuo Yang, Rahul Mangharam, Radu Grosu
Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents.
1 code implementation • 29 Aug 2023 • Daniel Scheuchenstuhl, Stefan Ulmer, Felix Resch, Luigi Berducci, Radu Grosu
In this paper, we propose a novel approach to model and emulate the human attention with an approximate prediction model.
no code implementations • 28 Aug 2023 • Julian Lemmel, Zahra Babaiee, Marvin Kleinlehner, Ivan Majic, Philipp Neubauer, Johannes Scholz, Radu Grosu, Sophie A. Neubauer
Modern tourism in the 21st century is facing numerous challenges.
no code implementations • 23 Aug 2023 • Sophie A. Neubauer, Radu Grosu
To this end, we review LRT-NG, SLR, and GoTube, algorithms for constructing a tight reachtube, that is, an over-approximation of the set of states reachable within a given time-horizon, and provide guarantees for the reachtube bounds.
no code implementations • 8 Mar 2023 • Julian Lemmel, Radu Grosu
First, they allow to pack more parameters for a given number of neurons and synapses.
1 code implementation • 28 Oct 2022 • Shrajan Bhandary, Zahra Babaiee, Dejan Kostyszyn, Tobias Fechter, Constantinos Zamboglou, Anca-Ligia Grosu, Radu Grosu
Despite the success of convolutional neural networks for 3D medical-image segmentation, the architectures currently used are still not robust enough to the protocols of different scanners, and the variety of image properties they produce.
no code implementations • 20 Oct 2022 • Zahra Babaiee, Lucas Liebenwein, Ramin Hasani, Daniela Rus, Radu Grosu
On CIFAR-10 dataset, without requiring a pre-trained baseline network, we obtain 1. 02% and 1. 19% accuracy gain and 52. 3% and 54% parameters reduction, on ResNet56 and ResNet110, respectively.
no code implementations • 20 Oct 2022 • Luigi Berducci, Radu Grosu
The automatic synthesis of a policy through reinforcement learning (RL) from a given set of formal requirements depends on the construction of a reward signal and consists of the iterative application of many policy-improvement steps.
no code implementations • 27 Jun 2022 • Julian Lemmel, Zahra Babaiee, Marvin Kleinlehner, Ivan Majic, Philipp Neubauer, Johannes Scholz, Radu Grosu, Sophie A. Neubauer
Modern tourism in the 21st century is facing numerous challenges.
no code implementations • 2 Jun 2022 • Mathias Lechner, Ramin Hasani, Zahra Babaiee, Radu Grosu, Daniela Rus, Thomas A. Henzinger, Sepp Hochreiter
Residual mappings have been shown to perform representation learning in the first layers and iterative feature refinement in higher layers.
no code implementations • 15 Apr 2022 • Zahra Babaiee, Lucas Liebenwein, Ramin Hasani, Daniela Rus, Radu Grosu
Moreover, by training the pruning scores of all layers simultaneously our method can account for layer interdependencies, which is essential to find a performant sparse sub-network.
1 code implementation • 29 Oct 2021 • Shrajan Bhandary, Zahra Babaiee, Dejan Kostyszyn, Tobias Fechter, Constantinos Zamboglou, Anca-Ligia Grosu, Radu Grosu
Despite the great success of convolutional neural networks (CNN) in 3D medical image segmentation tasks, the methods currently in use are still not robust enough to the different protocols utilized by different scanners, and to the variety of image properties or artefacts they produce.
1 code implementation • 6 Oct 2021 • Luigi Berducci, Edgar A. Aguilar, Dejan Ničković, Radu Grosu
The automatic synthesis of policies for robotic-control tasks through reinforcement learning relies on a reward signal that simultaneously captures many possibly conflicting requirements.
no code implementations • 21 Sep 2021 • Jie He, Ezio Bartocci, Dejan Ničković, Haris Isakovic, Radu Grosu
In this paper we propose DeepSTL, a tool and technique for the translation of informal requirements, given as free English sentences, into Signal Temporal Logic (STL), a formal specification language for cyber-physical systems, used both by academia and advanced research labs in industry.
1 code implementation • 18 Jul 2021 • Sophie Gruenbacher, Mathias Lechner, Ramin Hasani, Daniela Rus, Thomas A. Henzinger, Scott Smolka, Radu Grosu
Our algorithm solves a set of global optimization (Go) problems over a given time horizon to construct a tight enclosure (Tube) of the set of all process executions starting from a ball of initial states.
1 code implementation • 13 Jun 2021 • Zahra Babaiee, Ramin Hasani, Mathias Lechner, Daniela Rus, Radu Grosu
Robustness to variations in lighting conditions is a key objective for any deep vision system.
no code implementations • 15 Mar 2021 • Mathias Lechner, Ramin Hasani, Radu Grosu, Daniela Rus, Thomas A. Henzinger
Adversarial training is an effective method to train deep learning models that are resilient to norm-bounded perturbations, with the cost of nominal performance drop.
1 code implementation • 8 Mar 2021 • Axel Brunnbauer, Luigi Berducci, Andreas Brandstätter, Mathias Lechner, Ramin Hasani, Daniela Rus, Radu Grosu
World models learn behaviors in a latent imagination space to enhance the sample-efficiency of deep reinforcement learning (RL) algorithms.
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.
4 code implementations • 8 Jun 2020 • Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
We introduce a new class of time-continuous recurrent neural network models.
no code implementations • 26 Feb 2020 • Radu Grosu
This paper shows that ResNets, NeuralODEs, and CT-RNNs, are particular neural regulatory networks (NRNs), a biophysical model for the nonspiking neurons encountered in small species, such as the C. elegans nematode, and in the retina of large species.
no code implementations • 11 Oct 2019 • Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Graham W. Taylor, Sinead A. Williamson
As the availability and importance of temporal interaction data--such as email communication--increases, it becomes increasingly important to understand the underlying structure that underpins these interactions.
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.
no code implementations • 28 May 2019 • Elahe Ghalebi, Hamidreza Mahyar, Radu Grosu, Graham W. Taylor, Sinead A. Williamson
Interaction graphs, such as those recording emails between individuals or transactions between institutions, tend to be sparse yet structured, and often grow in an unbounded manner.
no code implementations • 1 Nov 2018 • Ramin M. Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
In this paper, we introduce the notion of liquid time-constant (LTC) recurrent neural networks (RNN)s, a subclass of continuous-time RNNs, with varying neuronal time-constant realized by their nonlinear synaptic transmission model.
no code implementations • 16 Oct 2018 • Denise Ratasich, Faiq Khalid, Florian Geissler, Radu Grosu, Muhammad Shafique, Ezio Bartocci
Furthermore, this paper presents the main challenges in building a resilient IoT for CPS which is crucial in the era of smart CPS with enhanced connectivity (an excellent example of such a system is connected autonomous vehicles).
1 code implementation • 11 Sep 2018 • Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce Neuronal Circuit Policies (NCPs), defined as the model of biological neural circuits reparameterized for the control of an alternative task.
no code implementations • 11 Sep 2018 • Ramin M. Hasani, Alexander Amini, Mathias Lechner, Felix Naser, Radu Grosu, Daniela Rus
In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level.
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 • NeurIPS 2018 • Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu, Jure Leskovec
We propose a novel framework for providing a non-parametric dynamic network model--based on a mixture of coupled hierarchical Dirichlet processes-- based on data capturing cascade node infection times.
1 code implementation • 22 Mar 2018 • Mathias Lechner, Ramin M. Hasani, Radu Grosu
We propose an effective way to create interpretable control agents, by re-purposing the function of a biological neural circuit model, to govern simulated and real world reinforcement learning (RL) test-beds.
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.
no code implementations • 9 Nov 2017 • Mathias Lechner, Radu Grosu, Ramin M. Hasani
We model the tap-withdrawal (TW) neural circuit of the nematode, \textit{C. elegans}, a circuit responsible for the worm's reflexive response to external mechanical touch stimulations, and learn its synaptic and neural parameters as a policy for controlling the inverted pendulum problem.
no code implementations • 4 Nov 2017 • Magdalena Fuchs, Manuel Zimmer, Radu Grosu, Ramin M. Hasani
Individual Neurons in the nervous systems exploit various dynamics.
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 • 18 Mar 2017 • Ramin M. Hasani, Guodong Wang, Radu Grosu
We demonstrate the superiority of the AEC over many other state-of-the-art approaches for the health monitoring and prognostic of machine bearings.
no code implementations • 18 Mar 2017 • Ramin M. Hasani, Victoria Beneder, Magdalena Fuchs, David Lung, Radu Grosu
We introduce SIM-CE, an advanced, user-friendly modeling and simulation environment in Simulink for performing multi-scale behavioral analysis of the nervous system of Caenorhabditis elegans (C. elegans).
no code implementations • 18 Mar 2017 • Ramin M. Hasani, Magdalena Fuchs, Victoria Beneder, Radu Grosu
Caenorhabditis elegans (C. elegans) illustrated remarkable behavioral plasticities including complex non-associative and associative learning representations.
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.
no code implementations • 5 Dec 2016 • Seyed Mohammad Taheri, Hamidreza Mahyar, Mohammad Firouzi, Elahe Ghalebi K., Radu Grosu, Ali Movaghar
Hence, we build our method, called Hell-TrustSVD, on top of the state-of-the-art social recommendation technique to incorporate both the extracted implicit social relations and ratings given by users on the prediction of items for an active user.
1 code implementation • 27 Oct 2015 • Alena Rodionova, Ezio Bartocci, Dejan Nickovic, Radu Grosu
We also provide a quantitative semantics for MTL, which measures the normalized, maximum number of times a formula is satisfied within its associated kernel, by a given signal.
Logic in Computer Science 03B44 F.4.1; D.3.1
no code implementations • 13 Feb 2015 • Konstantin Selyunin, Denise Ratasich, Ezio Bartocci, Radu Grosu
We introduce Deep Neural Programs (DNP), a novel programming paradigm for writing adaptive controllers for cy-ber-physical systems (CPS).
no code implementations • 24 Aug 2013 • Ezio Bartocci, Radu Grosu
We discuss the problem of runtime verification of an instrumented program that misses to emit and to monitor some events.