37 citations · 110 across the 20 of their papers we have counts for
7 papers · 1 filter
Using Part-based Representations for Explainable Deep Reinforcement Learning
Manos Kirtas, Konstantinos Tsampazis, Loukia Avramelou +2
Utilizing deep learning models to learn part-based representations holds significant potential for interpretable-by-design approaches, as these models incorporate latent causes obt…
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…
Non-Linear Spectral Dimensionality Reduction Under Uncertainty
Firas Laakom, Jenni Raitoharju, Nikolaos Passalis +2
In this paper, we consider the problem of non-linear dimensionality reduction under uncertainty, both from a theoretical and algorithmic perspectives. Since real-world data usually…
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…