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Found 12 papers, 2 papers with code
Dense Hopfield Networks in the Teacher-Student Setting
Dense Hopfield networks are known for their feature to prototype transition and adversarial robustness.
Hopfield model with planted patterns: a teacher-student self-supervised learning model
While Hopfield networks are known as paradigmatic models for memory storage and retrieval, modern artificial intelligence systems mainly stand on the machine learning paradigm.
Reinforcement Learning Policy Recommendation for Interbank Network Stability
The policy recommendation directs economic actors to create credit relationships through the optimal choice between a low interest rate or a high liquidity supply.
Deep Reinforcement Trading with Predictable Returns
Classical portfolio optimization often requires forecasting asset returns and their corresponding variances in spite of the low signal-to-noise ratio provided in the financial markets.
Contingent Convertible Bonds in Financial Networks
We study the role of contingent convertible bonds (CoCos) in a complex network of interconnected banks.
On the equivalence between the Kinetic Ising Model and discrete autoregressive processes
Binary random variables are the building blocks used to describe a large variety of systems, from magnetic spins to financial time series and neuron activity.
Statistical Mechanics Econometrics Data Analysis, Statistics and Probability
Modelling time-varying interactions in complex systems: the Score Driven Kinetic Ising Model
A common issue when analyzing real-world complex systems is that the interactions between the elements often change over time: this makes it difficult to find optimal models that describe this evolution and that can be estimated from data, particularly when the driving mechanisms are not known.
Unveiling the relation between herding and liquidity with trader lead-lag networks
We propose a method to infer lead-lag networks of traders from the observation of their trade record as well as to reconstruct their state of supply and demand when they do not trade.
A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market
We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-specific latent variables (dynamic fitnesses) describing the propensity of each node to create links.
Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield Networks with Arbitrary Priors
Restricted Boltzmann Machines are described by the Gibbs measure of a bipartite spin glass, which in turn corresponds to the one of a generalised Hopfield network.
Disentangling group and link persistence in Dynamic Stochastic Block models
We analytically and numerically characterize the detectability transitions of such algorithm as a function of the memory parameters of the model and we make a comparison with a full dynamic inference.
Phase transitions in Restricted Boltzmann Machines with generic priors
We study Generalised Restricted Boltzmann Machines with generic priors for units and weights, interpolating between Boolean and Gaussian variables.
