This review consolidates the theory and algorithms of Information Filtering Networks, arguing they offer an efficient, interpretable way to model high-dimensional dependencies and to build neural network structures.
Market structure dynamics during COVID-19 outbreak
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
In this note, we discuss the impact of the COVID-19 outbreak from the perspective of the market-structure. We observe that the US market-structure has dramatically changed during the past four weeks and that the level of change has followed the number of infected cases reported in the USA. Presently, market-structure resembles most closely the structure during the middle of the 2008 crisis but there are signs that it may be starting to evolve into a new structure altogether. This is the first article of a series where we will be analyzing and discussing market-structure as it evolves to a state of further instability or, more optimistically, stabilization and recovery.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Information Filtering Networks: Theoretical Foundations, Generative Methodologies, and Real-World Applications
This review consolidates the theory and algorithms of Information Filtering Networks, arguing they offer an efficient, interpretable way to model high-dimensional dependencies and to build neural network structures.