565 citations · 568 across the 8 of their papers we have counts for
9 papers
Navigating the Latent Space Dynamics of Neural Models
Marco Fumero, Luca Moschella, Emanuele Rodolà +1
Neural networks transform high-dimensional data into compact, structured representations, often modeled as elements of a lower dimensional latent space. In this paper, we present a…
Mapping representations in Reinforcement Learning via Semantic Alignment for Zero-Shot Stitching
Antonio Pio Ricciardi, Valentino Maiorca, Luca Moschella +2
Deep Reinforcement Learning (RL) models often fail to generalize when even small changes occur in the environment's observations or task requirements. Addressing these shifts typic…
Latent Space Translation via Inverse Relative Projection
Valentino Maiorca, Luca Moschella, Marco Fumero +2
The emergence of similar representations between independently trained neural models has sparked significant interest in the representation learning community, leading to the devel…
Latent Communication in Artificial Neural Networks
Luca Moschella
As NNs permeate various scientific and industrial domains, understanding the universality and reusability of their representations becomes crucial. At their core, these networks cr…
R3L: Relative Representations for Reinforcement Learning
Antonio Pio Ricciardi, Valentino Maiorca, Luca Moschella +2
Visual Reinforcement Learning is a popular and powerful framework that takes full advantage of the Deep Learning breakthrough. It is known that variations in input domains (e.g., d…
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…
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.…
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…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…