Zobrazeno 1 - 10
of 124
pro vyhledávání: '"Rodola, Emanuele"'
Autor:
Miranda, Michele, Ruzzetti, Elena Sofia, Santilli, Andrea, Zanzotto, Fabio Massimo, Bratières, Sébastien, Rodolà, Emanuele
Large Language Models (LLMs) represent a significant advancement in artificial intelligence, finding applications across various domains. However, their reliance on massive internet-sourced datasets for training brings notable privacy issues, which a
Externí odkaz:
http://arxiv.org/abs/2408.05212
The emergence of similar representations between independently trained neural models has sparked significant interest in the representation learning community, leading to the development of various methods to obtain communication between latent space
Externí odkaz:
http://arxiv.org/abs/2406.15057
Neural models learn data representations that lie on low-dimensional manifolds, yet modeling the relation between these representational spaces is an ongoing challenge. By integrating spectral geometry principles into neural modeling, we show that th
Externí odkaz:
http://arxiv.org/abs/2406.14183
In this paper, we present a novel data-free method for merging neural networks in weight space. Differently from most existing works, our method optimizes for the permutations of network neurons globally across all layers. This allows us to enforce c
Externí odkaz:
http://arxiv.org/abs/2405.17897
In this work, we present the local patch mesh representation for neural signed distance fields. This technique allows to discretize local regions of the level sets of an input SDF by projecting and deforming flat patch meshes onto the level set surfa
Externí odkaz:
http://arxiv.org/abs/2405.12895
Autor:
Postolache, Emilian, Polouliakh, Natalia, Kitano, Hiroaki, Connelly, Akima, Rodolà, Emanuele, Cosmo, Luca, Akama, Taketo
In this article, we explore the potential of using latent diffusion models, a family of powerful generative models, for the task of reconstructing naturalistic music from electroencephalogram (EEG) recordings. Unlike simpler music with limited timbre
Externí odkaz:
http://arxiv.org/abs/2405.09062
Autor:
Ciranni, Ruben, Postolache, Emilian, Mariani, Giorgio, Mancusi, Michele, Cosmo, Luca, Rodolà, Emanuele
We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive learning method for musical audio representations that captures the harmonic and rhythmic coherence between samples. Our method operates at the level of stems (or th
Externí odkaz:
http://arxiv.org/abs/2404.16969
Autor:
Ricciardi, Antonio Pio, Maiorca, Valentino, Moschella, Luca, Marin, Riccardo, Rodolà, Emanuele
Visual Reinforcement Learning is a popular and powerful framework that takes full advantage of the Deep Learning breakthrough. However, it is also known that variations in the input (e.g., different colors of the panorama due to the season of the yea
Externí odkaz:
http://arxiv.org/abs/2404.12917
Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks: generating a set of coherent sources, creating accompaniments, and performing source separation. Despite their versatility, they require estimating the joint distr
Externí odkaz:
http://arxiv.org/abs/2403.11706
We present GSEdit, a pipeline for text-guided 3D object editing based on Gaussian Splatting models. Our method enables the editing of the style and appearance of 3D objects without altering their main details, all in a matter of minutes on consumer h
Externí odkaz:
http://arxiv.org/abs/2403.05154