20 citations · 37 across the 15 of their papers we have counts for
16 papers
On the flow matching interpretability
Francesco Pivi, Simone Gazza, Davide Evangelista +2
Generative models based on flow matching have demonstrated remarkable success in various domains, yet they suffer from a fundamental limitation: the lack of interpretability in the…
Language Models Are Implicitly Continuous
Samuele Marro, Davide Evangelista, X. Angelo Huang +3
Language is typically modelled with discrete sequences. However, the most successful approaches to language modelling, namely neural networks, are continuous and smooth function ap…
Controlling Ensemble Variance in Diffusion Models: An Application for Reanalyses Downscaling
Fabio Merizzi, Davide Evangelista, Harilaos Loukos
In recent years, diffusion models have emerged as powerful tools for generating ensemble members in meteorology. In this work, we demonstrate how a Denoising Diffusion Implicit Mod…
Approximately optimal trade execution strategies under fast mean-reversion
David Evangelista, Yuri Thamsten
In a fixed time horizon, appropriately executing a large amount of a particular asset -- meaning a considerable portion of the volume traded within this frame -- is challenging. Es…
Ambiguity in solving imaging inverse problems with deep learning based operators
Davide Evangelista, Elena Morotti, Elena Loli Piccolomini +1
In recent years, large convolutional neural networks have been widely used as tools for image deblurring, because of their ability in restoring images very precisely. It is well kn…
Image Embedding for Denoising Generative Models
Andrea Asperti, Davide Evangelista, Samuele Marro +1
Denoising Diffusion models are gaining increasing popularity in the field of generative modeling for several reasons, including the simple and stable training, the excellent genera…
To be or not to be stable, that is the question: understanding neural networks for inverse problems
Davide Evangelista, James Nagy, Elena Morotti +1
The solution of linear inverse problems arising, for example, in signal and image processing is a challenging problem since the ill-conditioning amplifies, in the solution, the noi…
Price formation in financial markets: a game-theoretic perspective
David Evangelista, Yuri Saporito, Yuri Thamsten
We propose two novel frameworks to study the price formation of an asset negotiated in an order book. Specifically, we develop a game-theoretic model in many-person games and mean-…
RISING a new framework for few-view tomographic image reconstruction with deep learning
Davide Evangelista, Elena Morotti, Elena Loli Piccolomini
This paper proposes a new two-step procedure for sparse-view tomographic image reconstruction. It is called RISING, since it combines an early-stopped Rapid Iterative Solver with a…
Dissecting FLOPs along input dimensions for GreenAI cost estimations
Andrea Asperti, Davide Evangelista, Moreno Marzolla
The term GreenAI refers to a novel approach to Deep Learning, that is more aware of the ecological impact and the computational efficiency of its methods. The promoters of GreenAI…
A survey on Variational Autoencoders from a GreenAI perspective
A. Asperti, D. Evangelista, E. Loli Piccolomini
Variational AutoEncoders (VAEs) are powerful generative models that merge elements from statistics and information theory with the flexibility offered by deep neural networks to ef…
On finite population games of optimal trading
David Evangelista, Yuri Thamsten
We investigate stochastic differential games of optimal trading comprising a finite population. There are market frictions in the present framework, which take the form of stochast…
Closed-form approximations in multi-asset market making
Philippe Bergault, David Evangelista, Olivier Guéant +1
A large proportion of market making models derive from the seminal model of Avellaneda and Stoikov. The numerical approximation of the value function and the optimal quotes in thes…
Optimal inventory management and order book modeling
Nicolas Baradel, Bruno Bouchard, David Evangelista +1
We model the behavior of three agent classes acting dynamically in a limit order book of a financial asset. Namely, we consider market makers (MM), high-frequency trading (HFT) fir…
Radially Symmetric Mean-Field Games with Congestion
David Evangelista, Diogo A. Gomes, Levon Nurbekyan
Here, we study radial solutions for first- and second-order stationary Mean-Field Games (MFG) with congestion on . MFGs with congestion model problems where the agent…
On the existence of solutions for stationary mean-field games with congestion
David Evangelista, Diogo A. Gomes
Mean-field games (MFGs) are models of large populations of rational agents who seek to optimize an objective function that takes into account their location and the distribution of…