65 citations · 287 across the 27 of their papers we have counts for
7 papers · 1 filter
Multiplicative update rules for accelerating deep learning training and increasing robustness
Manos Kirtas, Nikolaos Passalis, Anastasios Tefas
Even nowadays, where Deep Learning (DL) has achieved state-of-the-art performance in a wide range of research domains, accelerating training and building robust DL models remains a…
Deep Learning for Energy Time-Series Analysis and Forecasting
Maria Tzelepi, Charalampos Symeonidis, Paraskevi Nousi +5
Energy time-series analysis describes the process of analyzing past energy observations and possibly external factors so as to predict the future. Different tasks are involved in t…
Autoencoder-driven Spiral Representation Learning for Gravitational Wave Surrogate Modelling
Paraskevi Nousi, Styliani-Christina Fragkouli, Nikolaos Passalis +5
Recently, artificial neural networks have been gaining momentum in the field of gravitational wave astronomy, for example in surrogate modelling of computationally expensive wavefo…
Attention-based Neural Bag-of-Features Learning for Sequence Data
Dat Thanh Tran, Nikolaos Passalis, Anastasios Tefas +2
In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on releva…
Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data
Nikolaos Passalis, Anastasios Tefas, Juho Kanniainen +2
Time series forecasting is a crucial component of many important applications, ranging from forecasting the stock markets to energy load prediction. The high-dimensionality, veloci…
Using Deep Learning for price prediction by exploiting stationary limit order book features
Avraam Tsantekidis, Nikolaos Passalis, Anastasios Tefas +3
The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowl…
Learning Deep Representations with Probabilistic Knowledge Transfer
Nikolaos Passalis, Anastasios Tefas
Knowledge Transfer (KT) techniques tackle the problem of transferring the knowledge from a large and complex neural network into a smaller and faster one. However, existing KT meth…