activity
20202026
most citedBeyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

565 citations · 663 across the 57 of their papers we have counts for

collaborators
Showing 2023Show all

14 papers · 1 filter

cs.LG2023

From Charts to Atlas: Merging Latent Spaces into One

Donato Crisostomi, Irene Cannistraci, Luca Moschella +4

Models trained on semantically related datasets and tasks exhibit comparable inter-sample relations within their latent spaces. We investigate in this study the aggregation of such…

eess.AS2023

Zero-Shot Duet Singing Voices Separation with Diffusion Models

Chin-Yun Yu, Emilian Postolache, Emanuele Rodolà +1

In recent studies, diffusion models have shown promise as priors for solving audio inverse problems. These models allow us to sample from the posterior distribution of a target sig…

physics.comp-ph2023

Efficient Generation of Multimodal Fluid Simulation Data

Daniele Baieri, Donato Crisostomi, Stefano Esposito +2

In this work, we introduce an efficient generation procedure to produce synthetic multi-modal datasets of fluid simulations. The procedure can reproduce the dynamics of fluid flows…

cs.LG2023★ 2 cited

Latent Space Translation via Semantic Alignment

Valentino Maiorca, Luca Moschella, Antonio Norelli +3

While different neural models often exhibit latent spaces that are alike when exposed to semantically related data, this intrinsic similarity is not always immediately discernible.…

cs.SD2023★ 2 cited

SyncFusion: Multimodal Onset-synchronized Video-to-Audio Foley Synthesis

Marco Comunità, Riccardo F. Gramaccioni, Emilian Postolache +3

Sound design involves creatively selecting, recording, and editing sound effects for various media like cinema, video games, and virtual/augmented reality. One of the most time-con…

cs.LG2023

From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication

Irene Cannistraci, Luca Moschella, Marco Fumero +2

It has been observed that representations learned by distinct neural networks conceal structural similarities when the models are trained under similar inductive biases. From a geo…