Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Cyranka, Jacek"'
Autor:
Cyranka, Jacek, Mucha, Piotr B.
We present an opinion model founded upon the principles of the bounded confidence interaction among agents. Our objective is to explain the polarization effects inherent to vector-valued opinions. The evolutionary process adheres to the rule where ea
Externí odkaz:
http://arxiv.org/abs/2312.14599
Autor:
Cyranka, Jacek, Haponiuk, Szymon
In order to support the advancement of machine learning methods for predicting time-series data, we present a comprehensive dataset designed explicitly for long-term time-series forecasting. We incorporate a collection of datasets obtained from diver
Externí odkaz:
http://arxiv.org/abs/2309.15946
Publikováno v:
Frontiers in Artificial Intelligence and Applications, ECAI 2023
We raise concerns about controllers' robustness in simple reinforcement learning benchmark problems. We focus on neural network controllers and their low neuron and symbolic abstractions. A typical controller reaching high mean return values still ge
Externí odkaz:
http://arxiv.org/abs/2307.15456
Autor:
Cyranka, Jacek, Mucha, Piotr B.
Publikováno v:
In Journal of the Franklin Institute November 2024 361(16)
Autor:
Polaczyk, Bartłomiej, Cyranka, Jacek
We study the overparametrization bounds required for the global convergence of stochastic gradient descent algorithm for a class of one hidden layer feed-forward neural networks, considering most of the activation functions used in practice, includin
Externí odkaz:
http://arxiv.org/abs/2201.12052
Autor:
Cyranka, Jacek, Lessard, Jean-Philippe
In this paper we introduce a new approach to compute rigorously solutions of Cauchy problems for a class of semi-linear parabolic partial differential equations. Expanding solutions with Chebyshev series in time and Fourier series in space, we introd
Externí odkaz:
http://arxiv.org/abs/2101.00684
Autor:
Gruenbacher, Sophie, Hasani, Ramin, Lechner, Mathias, Cyranka, Jacek, Smolka, Scott A., Grosu, Radu
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence, 35(13), 2021, pages 11525-11535
We show that Neural ODEs, an emerging class of time-continuous neural networks, can be verified by solving a set of global-optimization problems. For this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an abstraction-based technique
Externí odkaz:
http://arxiv.org/abs/2012.08863
Autor:
Gruenbacher, Sophie, Cyranka, Jacek, Lechner, Mathias, Islam, Md. Ariful, Smolka, Scott A., Grosu, Radu
Publikováno v:
Proceedings of the 59th IEEE Conference on Decision and Control (CDC), 2020, pages 1556-1563
We introduce LRT-NG, a set of techniques and an associated toolset that computes a reachtube (an over-approximation of the set of reachable states over a given time horizon) of a nonlinear dynamical system. LRT-NG significantly advances the state-of-
Externí odkaz:
http://arxiv.org/abs/2012.07458
Topological data analysis aims to extract topological quantities from data, which tend to focus on the broader global structure of the data rather than local information. The Mapper method, specifically, generalizes clustering methods to identify sig
Externí odkaz:
http://arxiv.org/abs/1910.08103
This work is motivated by the following question in data-driven study of dynamical systems: given a dynamical system that is observed via time series of persistence diagrams that encode topological features of solutions snapshots, what conclusions ca
Externí odkaz:
http://arxiv.org/abs/1810.12447