Artificial Intelligence & Information Analysis

2020

  1. N. Passalis, A.Tefas, J. Kanniainen, M. Gabbouj and A.Iosifidis, "Temporal logistic neural Bag-of-Features for financial time series forecasting leveraging limit order book data", Pattern Recognition Letters, pp 183-189, 2020.
  2. F. Laakom, N. Passalis, J. Raitoharju, J. Nikkanen, A.Tefas, A.Iosifidis and M. Gabbouj, "Bag of Color Features for Color Constancy", IEEE Transactions on Image Process, pp 7722-7734, 2020.
  3. A. Tsantekidis, N. Passalis, A.Tefas, J. Kanniainen, M. Gabbouj and A.Iosifidis, "Using Deep Learning for price prediction by exploiting stationary limit order book features", Applied Soft Computing, 2020.
  4. N. Passalis, M. Tzelepi and A.Tefas, "Probabilistic Knowledge Transfer for Lightweight Deep Representation Learning", IEEE Transactions on Neural Networks and Learning Systems, pp 1-10, 2020.
  5. N. Passalis, G. Mourgias-Alexandris, N. Pleros and A.Tefas, "Initializing Photonic Feed-forward Neural Networks using Auxiliary Tasks", Neural Networks, pp 103-108, 2020.
  6. A. Tsantekidis, N. Passalis, A. - S. Toufa, K. Saitas-Zarkias, S. Charistanidis and A.Tefas, "Price Trailing for Financial Trading using Deep Reinforcement Learning", Transactions on Neural Networks and Learning Systems, pp 1-10, 2020.
  7. M. Krestenitis, N. Passalis, A.Iosifidis, M. Gabbouj and A.Tefas, "Recurrent Bag-of-Features for Visual Information Analysis", Pattern Recognition, 2020.
  8. N. Passalis, J. Raitoharju, A.Tefas and M. Gabbouj, "Efficient Adaptive Inference for Deep Convolutional Neural Networks using Hierarchical Early Exits", Pattern Recognition, 2020.
  9. N. Passalis, A.Iosifidis, M. Gabbouj and A.Tefas, "Hypersphere-based Weight Imprinting for Few-shot Learning on Embedded Devices", Transactions on Neural Networks and Learning Systems, pp 1-6, 2020.
  10. A. Totovic, G. Dabos, N. Passalis, A.Tefas and N. Pleros, "Femtojoule per MAC Neuromorphic Photonics: An Energy and Technology Roadmap", IEEE Journal of Selected Topics in Quantum Electronics, pp 1-15, 2020.
  11. G. Mourgias-Alexandris, G. Dabos, N. Passalis, A. Totovic, A.Tefas and N. Pleros, "All-optical WDM Recurrent Neural Networks with Gating", IEEE Journal of Selected Topics in Quantum Electronics, 2020.
  12. G. Mourgias-Alexandris, N. Passalis, G. Dabos, A. Totovic, A.Tefas and N. Pleros, "A Photonic Recurrent Neuron for Time-Series Classification", Journal of Lightwave Technology, pp: 1-1, 2020.

2019

  1. N. Passalis, G. Mourgias-Alexandris, A. Tsakyridis, N. Pleros and A.Tefas, "Training Deep Photonic Convolutional Neural Networks with Sinusoidal Activations", IEEE Transactions on Emerging Topics in Computational Intelligence, pp 1-10, 2019.
  2. N. Passalis and A.Tefas, "Discriminative clustering using regularized subspace learning", Pattern Recognition, 2019.
  3. A. Papadimitriou, N. Passalis and A.Tefas, "Visual representation decoding from human brain activity using machine learning: A baseline study", Pattern Recognition Letters, pp. 28-44, 2019.
  4. G. Mourgias-Alexandris, A. Totovic, A. Tsakyridis, N. Passalis, A.Tefas and K. Vyrsokinos, "Neuromorphic Photonics with Coherent Linear Neuronsusing dual-IQ modulation cells", Journal of Lightware Technology, pp. 811-819, 2019.
  5. N. Passalis, A.Iosifidis, M. Gabbouj and A.Tefas, "Variance-preserving deep metric learning for content-based image retrieval", Pattern Recognition Letters, pp. 8-14, 2019.
  6. N. Passalis, A.Tefas, J. Kanniainen, M. Gabbouj and A.Iosifidis, "Deep Adaptive Input Normalization for Time Series Forecasting", IEEE Transactions on Neural Networks and Learning Systems, pp. 1-6, 2019.
  7. N. Passalis and A.Tefas, "Deep Reinforcement Learning for Controlling Frontal Person Close-up Shooting", Neurocomputing, pp. 37-47, 2019.
  8. P. Nousi, A. Tsantekidis, N. Passalis, A.Tefas, J. Kanniainen, M. Gabbouj and A.Iosifidis, "Machine Learning for Forecasting Mid Price Movement Using Limit Order Book Data", IEEE Access, 2019.
  9. G. Mourgias-Alexandris, A. Tsakyridis, N. Passalis, A.Tefas, K. Vyrsokinos and N. Pleros, "A Sigmoid Optical Neuron for Photonic Neural Network Applications", Optics Express, 2019.
  10. N. Passalis and A.Tefas, "Continuous drone control using deep reinforcement learning for frontal view person shooting", Springer Neural Computing and Applications, July, 2019.

2018

  1. V. Lioutas, N. Passalis and A.Tefas, "Explicit Ensemble Attention Learning for Improving Visual Question Answering", Pattern Recognition Letters, 2018.
  2. N. Passalis and A.Tefas, "Learning Bag-of-Embedded-Words Representations for Textual Information Retrieval", Pattern Recognition, pp. 254-267, 2018.
  3. N. Passalis and A.Tefas, "PySEF: A Python Library for Similarity-based Dimensionality Reduction", Knowledge-Based Systems, 2018.
  4. N. Passalis and A.Tefas, "Unsupervised Knowledge Transfer Using Similarity Embeddings", IEEE Transactions on Neural Networks and Learning Systems (TNNLS), pp. 946-950, 2018.
  5. N. Passalis and A.Tefas, "Long Term Temporal Averaging for Stochastic Optimization of Deep Neural Networks", Neural Computing and Applications, 2018 (accepted).
  6. N. Passalis and A.Tefas, "Training Lightweight Deep Convolutional Neural Networks Using Bag-of-Features Pooling", IEEE Transactions on Neural Networks and Learning Systems, 2018.
  7. N. Passalis, A.Tefas, J. Kanniainen, M. Gabbouj and A.Iosifidis, "Temporal Bag-of-Features Learning for Predicting Mid Price Movements Using High Frequency Limit Order Book Data", IEEE Transactions on Emerging Topics in Computational Intelligence, 2018.
  8. D. Spathis, N. Passalis and A.Tefas, "Interactive Dimensionality Reduction Using Similarity Projections", Knowledge-Based Systems, 2018.

2017

  1. N. Passalis and A.Tefas, "Dimensionality Reduction using Similarity-induced Embeddings", IEEE Transactions on Neural Networks and Learning Systems, 2017.
  2. N. Passalis and A.Tefas, "Learning Neural Bag-of-Features for Large-Scale Image Retrieval", IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017.

2016

  1. N. Passalis and A.Tefas, "Entropy Optimized Feature-Based Bag-of-Words Representation for Information Retrieval", IEEE Transactions on Knowledge and Data Engineering, pp. 1664-1677, 2016.
  2. N. Passalis and A.Tefas, "Information Clustering Using Manifold-Based Optimization of the Bag-of-Features Representation", IEEE Transactions on Cybernetics, 2016.
  3. N. Passalis and A.Tefas, "Neural Bag-of-Features Learning", Pattern Recognition, 2016.

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