2022

• SuperCoder: Program learning under noisy conditions from superposition of states

  A. Davody, M. Safari, & R. V. Florian. (2022)

  Neurocomputing, 489, 323-332.
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2021

• Tikhonov Regularization of a Perturbed Heavy Ball System with Vanishing Damping

  C. D. Alecsa, & S. C. László. (2021)

  SIAM Journal on Optimization, 31(4), 2921–2954.
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• Hopfield Neuronal Network of Fractional Order: A note on its numerical integration

  M. - F. Danca. (2021)

  Chaos, Solitons & Fractals, 151(111219).
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• Mitigating ageing bias in article level metrics using citation network analysis

  I. Tóth, Z. I. Lázár, L. Varga, F. Járai-Szabó, I. Papp, R. V. Florian, & M. Ercsey-Ravasz. (2021)

  Journal of Informetrics, 15(1), 101105.
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• Asymptotic analysis of a structure-preserving integrator for damped Hamiltonian systems

  A. Viorel, C. D. Alecsa, & T. O. Pința. (2021)

  Discrete & Continuous Dynamical Systems, 41(7), 3319-3341.
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• Coexisting Hidden and self-excited attractors in an economic system of integer or fractional order

  M. - F. Danca. (2021)

  International Journal of Bifurcation and Chaos, 31(4), 2150062.
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• Changing the Geometry of Representations: α-Embeddings for NLP Tasks

  R. Volpi, U. Thakur, & L. Malagò. (2021)

  Entropy, 23(3), 287.
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• Hidden Strange Nonchaotic Attractors

  M. - F. Danca, & N. Kuznetsov. (2021)

  Mathematics, 9(6), 652.
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• Three-dimensional periodontal investigations using a prototype handheld ultrasound scanner with spatial positioning reading sensor

  R. Chifor, M. Li, K. - C. T. Nguyen, T. Arsenescu, I. Chifor, A. F. Badea, .... (2021)

  Medical Ultrasonography.
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• Attractor as a convex combination of a set of attractors

  M. - F. Danca, M. Feckan, N. Kuznetsov, & G. Chen. (2021)

  Communications in Nonlinear Science and Numerical Simulation, 96, 105721.
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• Hidden and self-exited attractors in a heterogeneous Cournot oligopoly model

  M. - F. Danca, & M. Lampart. (2021)

  Chaos, Solitons & Fractals, 142, 110371.
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• Coupled Discrete Fractional-Order Logistic Maps

  M. - F. Danca, M. Feckan, N. Kuznetsov, & G. Chen. (2021)

  Mathematics, 9(18), 2204.
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• Matlab code for Lyapunov exponents of fractional-order systems, Part II: The non-commensurate case

  M. - F. Danca. (2021)

  International Journal of Bifurcation and Chaos, 31(12), 2150187.
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• Modal Functional ('Dialectica') Interpretation

  D. Hernest, & T. Trifonov. (2021)

  arXiv:1212.0020.
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2020

• Bifurcation and Chaos in Fractional- Order Systems

  Bifurcation and Chaos in Fractional- Order Systems. (2020)

  (M. - F. Danca & G. Chen, Eds.) Basel: MDPI.
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• Puu system of fractional order and its chaos suppression

  M. - F. Danca. (2020)

  Symmetry, 12(3), 340.
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• Chaos suppression in a Gompertz-like discrete system of fractional order

  M. - F. Danca, & M. Feckan. (2020)

  International Journal of Bifurcation and Chaos, 30(3), 2050049.
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• Constraining the Reionization History using Bayesian Normalizing Flows

  H. Hortúa, L. Malagò, & R. Volpi. (2020)

  Machine Learning: Science and Technology, 1, 035014.
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• Biharmonic δ(r)-ideal hypersurfaces in Euclidean spaces are minimal

  Deepika, & A. Arvanitoyeorgos. (2020)

  Differential Geometry and its Applications, 72.
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• Early Pleistocene freshwater fishes of Copăceni (Dacian Basin, southern Romania)

  Ş. Vasile, O. Kovalchuk, A. Petculescu, & M. Venczel. (2020)

  Paleontologia Electronica.
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• Management of water bodies in show caves – A microbial approach

  O. T. Moldovan, S. Bercea, R. Năstase-Bucur, S. Constantin, M. Kenesz, I. C. Mirea, .... (2020)

  Tourism Management, 78, 104037.
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• An extension of the second order dynamical system that models Nesterov’s convex gradient method

  C. D. Alecsa, S. C. László, & T. Pința. (2020)

  Applied Mathematics & Optimization.
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• Automated detection of presymptomatic conditions in spinocerebellar ataxia type 2 using Monte Carlo dropout and deep neural network techniques with electrooculogram signals

  C. Stoean, R. Stoean, M. Atencia, M. Abdar, L. Velázquez-Pérez, A. Khosravi, .... (2020)

  Sensors, 20(11), 3032.
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• New optimization algorithms for neural network training using operator splitting techniques

  C. D. Alecsa, T. Pința, & I. Boros. (2020)

  Neural Networks, 126, 178-190.
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• Automatic Feature Extraction for Phonocardiogram Heartbeat Anomaly Detection using WaveNet VAE

  R. - G. Colț, & C. - H. Várady. (2020)

  In Machine Learning for Healthcare and Pharma Applications ECML PKDD 2020 Workshop.
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• Detection of Tumours in Brain MRIs with Variational AutoEncoders

  A. - I. Albu, A. Enescu, & L. Malagò. (2020)

  In Machine Learning for Healthcare and Pharma Applications ECML PKDD 2020 Workshop.
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• Reliable Uncertainties for Bayesian Neural Networks using Alpha-divergences

  H. J. Hortúa, L. Malagò, & R. Volpi. (2020)

  In ICML 2020 Workshop on Uncertainty and Robustness in Deep Learning.
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• Evaluating Natural Alpha Embeddings on Intrinsic and Extrinsic Tasks

  R. Volpi, & L. Malagò. (2020)

  In 5th Workshop on Representation Learning for NLP .
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• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations

  A. Albu, A. Enescu, & L. Malagò. (2020)

  In 2020 KDD Workshop on Applied Data Science for Healthcare.
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• Parameters Estimation from the 21 cm signal using Variational Inference

  H. J. Hortúa, R. Volpi, & L. Malagò. (2020)

  In ICLR 2020 Fundamental Science in the era of AI .
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• Performance evaluation of Graph Convolutional Networks with Siamese training for few-shot classification of nodes

  N. Uțiu. (2020)

  In Bridge between perception and reasoning: Graph neural networks and beyond - ICML 2020 workshop.
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• Tumor Detection in Brain MRIs by Computing Dissimilarities in the Latent Space of a Variational AutoEncoder

  A. I. Albu, A. Enescu, & L. Malagò. (2020)

  In Proceedings of the Northern Lights Deep Learning WorkshopTromsø, Norway: Septentrio Academic Publishing.
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• Evaluating the Robustness of Defense Mechanisms based on AutoEncoder Reconstructions against Carlini-Wagner Adversarial Attacks

  P. Hlihor, R. Volpi, & L. Malagò. (2020)

  In Proceedings of the Northern Lights Deep Learning WorkshopTromsø, Norway: Proceedings of the Northern Lights Deep Learning Workshop.
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• DyNODE: Neural Ordinary Differential Equations for Dynamics Modeling in Continuous Control

  V. M. Martinez Alvarez, R. Roșca, & C. G. Fălcuțescu. (2020)

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• Training highly effective connectivities within neural networks with randomly initialized, fixed weights

  C. Ivan, & R. V. Florian. (2020)

  arXiv:2006.16627.
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• Lagrangian and Hamiltonian Mechanics for Probabilities on the Statistical Manifold

  G. Chirco, L. Malagò, & G. Pistone. (2020)

  ArXivarXiv:2009.09431.
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• Natural Wake-Sleep Algorithm

  C. Várady, R. Volpi, L. Malagò, & N. Ay. (2020)

  arXiv:2008.06687.
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• The long time behavior and the rate of convergence of symplectic convex algorithms obtained via splitting discretizations of inertial damping systems

  C. D. Alecsa. (2020)

  arXiv:2001.10831.
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2019

• Graphical structure of attraction basins of hidden attractors: the Rabinovich-Fabrikant system

  M. - F. Danca, P. Bourke, & N. Kuznetsov. (2019)

  International Journal of Bifurcation and Chaos, 29(1), 1930001.
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• Difference equations with impulses

  M. - F. Danca, & M. Feckan. (2019)

  Opuscula Mathematica, 39(1), 5-22.
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• Chaos control in the fractional order logistic map via impulses

  M. - F. Danca, M. Feckan, & N. Kuznetsov. (2019)

  Nonlinear Dynamics, 98(2), 1219–1230.
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• Rich dynamics and anticontrol of extinction in a prey-predator system

  M. - F. Danca, M. Feckan, N. Kuznetsov, & G. Chen. (2019)

  Nonlinear Dynamics, 98(2), 1421–1445.
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• Hidden chaotic attractors and chaos suppression in an impulsive discrete economical supply and demand dynamical system

  M. - F. Danca, & M. Feckan. (2019)

  Communications in Nonlinear Science and Numerical Simulation, 74( ), 1-13.
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• The Growth and Decline of the Western Roman Empire

  S. Roman, & E. Palmer. (2019)

  Cliodynamics, 10(2).
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• Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations

  C. Stoean, W. Paja, R. Stoean, & A. Săndiță. (2019)

  PLOS One, 14(10), e0223593.
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• Deep Knowledge Based Agent: Learning to do tasks by self-thinking about imaginary worlds

  A. Davody. (2019)

  In Reinforcement Learning for Real Life ICML 2019 Workshop.
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• Analyzing the theoretical grounds of curiosity-driven, intrinsically-motivated learning

  R. V. Florian. (2019)

  In The Fourth International Workshop on Intrinsically Motivated Open-ended Learning (IMOL 2019).
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• Convolutional Neural Networks on Randomized Data

  C. Ivan. (2019)

  In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (p. 1-8).
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• On modelling emergence of logical thinking

  C. Ivan, & B. Indurkhya. (2019)

  In Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI. AAAI Spring Symposium.
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• Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks

  H. J. Hortúa, R. Volpi, D. Marinelli, & L. Malagò. (2019)

  arXiv:1911.08508.
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