2020

• Constraining the Reionization History using Bayesian Normalizing Flows

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

  IOP Journal Machine Learning: Science and Technology, 1.
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• Lagrangian and Hamiltonian Mechanics for Probabilities on the Statistical Manifold

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

  ArXiv.
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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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• Natural Wake-Sleep Algorithm

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

  arXiv:2008.06687.
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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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• 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

• 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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• Natural Alpha Embeddings

  R. Volpi, & L. Malagò. (2019)

  arXiv:1912.02280.
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2018

• Wasserstein Riemannian geometry of Gaussian densities

  L. Malagò, L. Montrucchio, & G. Pistone. (2018)

  Information Geometry, 1(2), 137–179.
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• A continuous-time MaxSAT solver with high analog performance

  B. Molnár, F. Molnár, M. Varga, & M. Ercsey-Ravasz. (2018)

  Nature Communications, 9, 4864.
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• Approximating hidden chaotic attractors via parameter switching

  M. - F. Danca, N. Kuznetsov, & G. Chen. (2018)

  Chaos, 28, 013127.
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• The mouse cortical connectome, characterized by an ultra-dense cortical graph, maintains specificity by distinct connectivity profiles

  R. Gămănuţ, H. Kennedy, Z. Toroczkai, M. Ercsey-Ravasz, D. C. Van Essen, K. Knoblauch, & A. Burkhalter. (2018)

  Neuron, 97(3), 698-715.
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• Generalization of the Filippov method for systems with large periodic input

  C. Morel, & M. - F. Danca. (2018)

  Mathematics and Computers in Simulation, 146, 1-13.
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• Fractional-order PWC systems without zero Lyapunov exponents

  M. - F. Danca, N. Kuznetsov, & G. Chen. (2018)

  Nonlinear Dynamics, 92(3), 1061–1078.
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• Lyapunov exponents of a hyperchaotic discontinuous 4D system of integer or fractional order

  M. - F. Danca. (2018)

  Entropy, 20, 337.
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• Modeling cognitive processes underlying computer programming

  C. F. Perțicaș, & B. Indurkhya. (2018)

  In 29th Annual Workshop of the Psychology of Programming Interest Group - PPIG 2018 (p. 16-27).
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• Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds

  S. Sârbu, R. Volpi, A. Pește, & L. Malagò. (2018)

  In ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models.
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• Using sequence to sequence neural networks for solving similar mathematical problems

  A. Davody, & M. S. Baba. (2018)

  In Working Papers and Documents of the IJCAI-ECAI-2018 Workshop on Learning and Reasoning: Principles & Applications to Everyday Spatial and Temporal Knowledge: L & R - 2018 (p. 30-38).
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• Learning to generate HTML code from images with no supervisory data

  A. Davody, H. Davoudi, M. S. Baba, & R. V. Florian. (2018)

  In Neural Abstract Machines & Program Induction v2: A Federated Artificial Intelligence Meeting (FAIM) workshop (ICML, IJCAI/ECAI, AAMAS).
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• Hierarchical segmentation of graphical interfaces for Document Object Model reconstruction

  C. F. Perțicaș, M. S. Baba, H. Davoudi, & R. V. Florian. (2018)

  In Neural Abstract Machines & Program Induction v2: A Federated Artificial Intelligence Meeting (FAIM) workshop (ICML, IJCAI/ECAI, AAMAS).
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• Applications of the f-divergence to variational inference

  S. Roman, & A. Pește. (2018)

  In ICML 2018 Workshop on Geometry in Machine Learning (GiMLi 2018).
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2017

• Software categorization using low-level distributional features

  Z. Bodó, & B. Indurkhya. (2017)

  In New Trends in Intelligent Software Methodologies, Tools and Techniques: Proceedings of the 16th International Conference (SoMeT_17) (Vol. 297, p. 88-98)Presented at the 16th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SOMET_17), IOS Press.
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• Impulsive stabilization of chaos in fractional-order systems

  M. - F. Danca, M. Fečkan, & G. Chen. (2017)

  Nonlinear Dynamics, 89(3), 1889–1903.
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• Unusual dynamics and hidden attractors of the Rabinovich–Fabrikant system

  M. - F. Danca, N. Kuznetsov, & G. Chen. (2017)

  Nonlinear Dynamics, 88(1), 791–805.
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• A multiscale cerebral neurochemical connectome of the rat brain

  H. R. Noori, J. Schöttler, M. Ercsey-Ravasz, A. Cosa-Linan, M. Varga, Z. Toroczkai, & R. Spanagel. (2017)

  PLoS Biology, 15(7), e2002612.
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• Hidden chaotic attractors in fractional-order systems

  M. - F. Danca. (2017)

  Nonlinear Dynamics, 89(1), 577–586.
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• Impulsive stabilization of chaos in fractional-order systems

  M. - F. Danca, M. Feckan, & G. Chen. (2017)

  Nonlinear Dynamics, 89(3), 1889–1903.
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• On the numerical integration of discontinuous dynamical systems

  M. - F. Danca, & M. Feckan. (2017)

  International Journal of Bifurcation and Chaos, 27(14), 1750218.
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• Parameter Switching Synchronization

  M. - F. Danca, & N. Kuznetsov. (2017)

  Applied Mathematics and Computation, 313, 94-102.
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• An Explanatory Analysis of the Geometry of Latent Variables Learned by Variational Auto-Encoders

  A. Pește, L. Malagò, & S. Sârbu. (2017)

  In NIPS 2017 Workshop on Bayesian Deep Learning.
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