37 citations · 53 across the 8 of their papers we have counts for
4 papers · 1 filter
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…