485,387 research outputs found
Two types of densification scaling in the evolution of temporal networks
Many real-world social networks constantly change their global properties
over time, such as the number of edges, size and density. While temporal and
local properties of social networks have been extensively studied, the origin
of their dynamical nature is not yet well understood. Networks may grow or
shrink if a) the total population of nodes changes and/or b) the chance of two
nodes being connected varies over time. Here, we develop a method that allows
us to classify the source of time-varying nature of temporal networks. In doing
so, we first show empirical evidence that real-world dynamical systems could be
categorized into two classes, the difference of which is characterized by the
way the number of edges grows with the number of active nodes, i.e.,
densification scaling. We develop a dynamic hidden-variable model to formally
characterize the two dynamical classes. The model is fitted to the empirical
data to identify whether the origin of scaling comes from a changing population
in the system or shifts in the connecting probabilities.Comment: 12 pages, 6 figures (plus 7 figures in SI
Learning to Play Othello with N-Tuple Systems
This paper investigates the use of n-tuple systems as position value functions for the game of Othello. The architecture is described, and then evaluated for use with temporal difference learning. Performance is compared with previously de-veloped weighted piece counters and multi-layer perceptrons. The n-tuple system is able to defeat the best performing of these after just five hundred games of self-play learning. The conclusion is that n-tuple networks learn faster and better than the other more conventional approaches
Comparing deep learning models for volatility prediction using multivariate data
This study aims at comparing several deep learning-based forecasters in the
task of volatility prediction using multivariate data, proceeding from simpler
or shallower to deeper and more complex models and compare them to the naive
prediction and variations of classical GARCH models. Specifically, the
volatility of five assets (i.e., S\&P500, NASDAQ100, gold, silver, and oil) was
predicted with the GARCH models, Multi-Layer Perceptrons, recurrent neural
networks, Temporal Convolutional Networks, and the Temporal Fusion Transformer.
In most cases the Temporal Fusion Transformer followed by variants of Temporal
Convolutional Network outperformed classical approaches and shallow networks.
These experiments were repeated, and the difference between competing models
was shown to be statistically significant, therefore encouraging their use in
practice
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