Zobrazeno 1 - 10
of 338
pro vyhledávání: '"Michiardi, Pietro"'
Tabular data is ubiquitous in many real-life systems. In particular, time-dependent tabular data, where rows are chronologically related, is typically used for recording historical events, e.g., financial transactions, healthcare records, or stock hi
Externí odkaz:
http://arxiv.org/abs/2406.15327
Diffusion models for Text-to-Image (T2I) conditional generation have seen tremendous success recently. Despite their success, accurately capturing user intentions with these models still requires a laborious trial and error process. This challenge is
Externí odkaz:
http://arxiv.org/abs/2405.20759
The analysis of scientific data and complex multivariate systems requires information quantities that capture relationships among multiple random variables. Recently, new information-theoretic measures have been developed to overcome the shortcomings
Externí odkaz:
http://arxiv.org/abs/2402.05667
Data Augmentation (DA) -- enriching training data by adding synthetic samples -- is a technique widely adopted in Computer Vision (CV) and Natural Language Processing (NLP) tasks to improve models performance. Yet, DA has struggled to gain traction i
Externí odkaz:
http://arxiv.org/abs/2401.10754
Data Augmentation (DA)-augmenting training data with synthetic samples-is wildly adopted in Computer Vision (CV) to improve models performance. Conversely, DA has not been yet popularized in networking use cases, including Traffic Classification (TC)
Externí odkaz:
http://arxiv.org/abs/2310.13935
In this work we present a new method for the estimation of Mutual Information (MI) between random variables. Our approach is based on an original interpretation of the Girsanov theorem, which allows us to use score-based diffusion models to estimate
Externí odkaz:
http://arxiv.org/abs/2310.09031
Multi-modal data-sets are ubiquitous in modern applications, and multi-modal Variational Autoencoders are a popular family of models that aim to learn a joint representation of the different modalities. However, existing approaches suffer from a cohe
Externí odkaz:
http://arxiv.org/abs/2306.04445
Generative Models (GMs) have attracted considerable attention due to their tremendous success in various domains, such as computer vision where they are capable to generate impressive realistic-looking images. Likelihood-based GMs are attractive due
Externí odkaz:
http://arxiv.org/abs/2305.18900
Autor:
Franzese, Giulio, Corallo, Giulio, Rossi, Simone, Heinonen, Markus, Filippone, Maurizio, Michiardi, Pietro
We introduce Functional Diffusion Processes (FDPs), which generalize score-based diffusion models to infinite-dimensional function spaces. FDPs require a new mathematical framework to describe the forward and backward dynamics, and several extensions
Externí odkaz:
http://arxiv.org/abs/2303.00800
While the promises of Multi-Task Learning (MTL) are attractive, characterizing the conditions of its success is still an open problem in Deep Learning. Some tasks may benefit from being learned together while others may be detrimental to one another.
Externí odkaz:
http://arxiv.org/abs/2301.02873